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==1 Title, abstract and keywords<!-- Your document should start with a concise and informative title. Titles are often used in information-retrieval systems. Avoid abbreviations and formulae where possible. Capitalize the first word of the title.
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<!-- metadata commented in wiki content
  
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Provide a maximum of 6 keywords, and avoiding general and plural terms and multiple concepts (avoid, for example, 'and', 'of'). Be sparing with abbreviations: only abbreviations firmly established in the field should be used. These keywords will be used for indexing purposes.
 
  
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An abstract is required for every document; it should succinctly summarize the reason for the work, the main findings, and the conclusions of the study. Abstract is often presented separately from the article, so it must be able to stand alone. For this reason, references and hyperlinks should be avoided. If references are essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself. -->==
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<big>'''Quantitative Analysis of Microvascular Invasion Preoperative Prediction in Solitary Small Hepatocellular Carcinoma on Dynamic Enhancement MRI'''</big>
  
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'''Xueyan ZHOU <sup>1, †</sup>, Xinxin WANG <sup>2, †</sup>, Zehong LIN<sup>1, 3</sup> and Yang ZHOU <sup>2,</sup>*'''
  
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<sup>1</sup>College of Engineering, Harbin University, Harbin, China;
  
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<sup>2</sup>Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
  
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==2 The main text<!-- You can enter and format the text of this document by selecting the ‘Edit’ option in the menu at the top of this frame or next to the title of every section of the document. This will give access to the visual editor. Alternatively, you can edit the source of this document (Wiki markup format) by selecting the ‘Edit source’ option.
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<sup>3</sup> College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
  
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Most of the documents in Scipedia are written in English (write your manuscript in American or British English, but not a mixture of these). Anyhow, specific publications in other languages can be published in Scipedia. In any case, the documents published in other languages must have an abstract written in English.
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<sup>†</sup>Xueyan ZHOU and Xinxin WANG contributed equally to this work.
  
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'''*''' Correspondence: Yang ZHOU (E-mail: [mailto:zhouyang094@126.com zhouyang094@126.com]).
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-->
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==Abstract==
  
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2.1 Subsections
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The purpose of this study is to predict preoperatively microvascular invasion (MVI) of solitary small hepatocellular cancer (sHCC) by using the kinetic parameters analysis on dynamic enhancement magnetic resonance imaging (MRI). Patients (<math>n = 61</math>) with known solitary sHCC(<math>\le 3</math>cm) were preoperatively examined with Gd-EOB-DTPA-enhanced MRI first before hepatic resection. The arterial peritumoral enhancement measured from the dynamic enhancement-MRI was analyzed by using quantitative kinetic parameters, including initial enhancement (<math>E_1</math>), peak enhancement (<math>E_{\rm peak}</math>), and enhancement ratio (<math>E_R</math>) calculated. Correlations between quantitative kinetic parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed. Histopathological analysis of liver resection confirmed that 19 patients had sHCC with MVI and that 42 patients had sHCC without MVI. Average (<math>\pm</math> standard deviation) <math>E_1</math> is <math>0.36\pm 0.12</math> and <math>0.46\pm 0.09</math>, <math>E_{\rm peak}</math> is <math>0.78\pm 0.24</math> and <math>0.74\pm 0.18</math>, and <math>E_R</math> is <math>0.42\pm 0.20</math> and <math>0.56\pm 0.17</math> for negative and positive group, respectively. Statistical analysis showed that average <math>E_1</math> and ER for the positive group were significantly higher (<math>p < 0.05</math>) than the negative group. The receiver operating characteristics (ROC) analysis between the two groups had area under the curve of 0.74 and 0.71 for <math>E_1</math> and <math>E_R</math>, respectively. Quantitative kinetic parameters analysis for the arterial peritumoral enhancement is feasibility to the prediction and assist diagnosis of MVI in clinical practice.
  
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Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1, 1.2, etc. and then 1.1.1, 1.1.2, ... Use this numbering also for internal cross-referencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Capitalize the first word of the headings.
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'''Keywords:''' Microvascular invasion, solitary small hepatocellular cancer, quantitative analysis, kinetic parameters, dynamic enhancement magnetic resonance imaging
  
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==1. Introduction==
  
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2.2 General guidelines
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<span id='OLE_LINK1'></span><span id='OLE_LINK2'></span>
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Microvessel invasion (MVI) is a major prognostic factor in hepatocellular carcinoma (HCC) that influences the choice of treatment, but rarely can be evaluated preoperatively [1,2]. Several studies have reported that certain imaging findings on dynamic enhancement MRI are useful for predicting MVI of HCC, including tumor size [3], peritumoral enhancement [4], tumor margin [5], tumor hypointensity or peritumoral hypointensity on hepatobiliary phase (HBP) [6,7], radiological capsule on gadoxetic acid–enhanced MR imaging and so on [8].
  
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Some general guidelines that should be followed in your manuscripts are:
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Gd-EOB-DTPA enhanced MRI can provide more valuable information for the assessment of HCC and has been widely used in preoperative evaluation settings [9,10]. Some researches use the HBP images to describe the tumor margins in the axial and coronal hepatobiliary phase, including smooth [6], wedge-shaped peritumoral enhancement [11] and irregular circumferential peritumoral enhancement [12].
  
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*  Avoid hyphenation at the end of a line.
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However, all of these studies are based on qualitative observations and lack the kinetic parameters analysis of arterial phase tumor peritumoral enhancement on MVI. Especially for the small hepatocellular carcinoma (sHCC) with the maximum tumor diameter <math>\le 3</math>cm [13], there are limited studies that have investigated MR imaging finding for predicting MVI [11]. Therefore, techniques for quantitative analysis of dynamic enhancement MRI should be developed to directly predict MVI of sHCC, which results in early recurrence after hepatic resection and poor prognosis [13].
  
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* Symbols denoting vectors and matrices should be indicated in bold type. Scalar variable names should normally be expressed using italics.
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To our knowledge this is the first study to quantitatively preoperatively predict solitary sHCC with MVI based on kinetic parameters analysis of dynamic enhancement MRI. The kinetic analysis of pre-enhanced phase, arterial phase (20 seconds), portal venous phase (55 seconds), equilibrium phase (90 seconds) and delayed phase (180 seconds) enhancement curves of dynamic enhancement MRI were used in this study.
  
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*  Use decimal points (not commas); use a space for thousands (10 000 and above).
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==2. Materials and methods ==
  
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*  Follow internationally accepted rules and conventions. In particular use the international system of units (SI). If other quantities are mentioned, give their equivalent in SI.
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This study was approved by the Institutional Review Board at our institution and the requirement for informed consent was waived. Patients were recruited from February 2018 to October 2019. The dynamic enhancement MRI was performed within one week before curative hepatectomy, and there is no macroscopic vascular invasion on MRI. A total of 61 patients (average age = 56 years old; 15 females and 46 males) with known single sHCC (<math>\pm 3</math>cm) were enrolled in this study. Tumor size, number, and capsule condition were obtained at gross specimen. The entire tumor was examined for sHCC. MVI was defined as the presence of tumor in vascular space of surrounding hepatic tissue line by endothelial cells on microscopy [14].
  
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MRI examination was performed by using 3.0-T system (Ingenia, Philips Medical Systems, Eindhoven, and The Netherlands) in all patients. 32-channel phased-array coil was used. The scanning scale covered from the top to the lower edge of live. The protocol used consisted of the following sequences which were shown in Table 1.
  
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2.3 Tables, figures, lists and equations
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<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
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<span style="text-align: center; font-size: 75%;">'''Table 1.''' ''' '''The protocol used consisted of the following sequences </span></div>
  
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Please insert tables as editable text and not as images. Tables should be placed next to the relevant text in the article. Number tables consecutively in accordance with their appearance in the text and place any table notes below the table body. Be sparing in the use of tables and ensure that the data presented in them do not duplicate results described elsewhere in the article.
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{| style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:80%;"
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|-
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|  style="border-top: 2pt solid black;border-bottom: 2pt solid black;width:800px;"|'''Sequences'''
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|  style="border-top: 2pt solid black;border-bottom: 2pt solid black;width:300px;"|'''Index'''
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|  style="border-top: 2pt solid black;border-bottom: 2pt solid black;width:300px;"|'''Value'''
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|-
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|  rowspan='4' style="border-top: 1pt solid black;padding-right:15px;border-bottom: 0pt solid black;"|Axial fat-suppressed RT T2W single shot turbo spin echo
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|  style="border-top: 1pt solid black;"|TR/ TE
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|  style="border-top: 1pt solid black;"|<math>535/75</math>
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|-
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| slice thickness /gap
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| <math>7/1</math> mm
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|-
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| FOV
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| <math>350\times 392</math> cm
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|-
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| style="border-bottom: 0pt solid black;"| matrix size
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| style="border-bottom: 0pt solid black;"| <math>232\times 199</math>
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|-
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| style="height:20px"|
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|-
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|  rowspan='4' style="width:400px;padding-right:15px;border-bottom: 0pt solid black;"|Coronal breath-hold T2W single shot turbo spin echo
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| TR/TE
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| <math>1100/80</math>
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|-
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| slice thickness /gap
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| <math>6/1</math> mm
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|-
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| FOV
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|<math> 350\times 346</math> cm
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|-
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| style="border-bottom: 0pt solid black;"| matrix size
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| style="border-bottom: 0pt solid black;"| <math>292\times 253</math>
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|-
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| style="height:20px"|
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|-
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|  rowspan='4' style="width:400px;text-align:left;padding-right:15px;border-bottom: 0pt solid black;"|Axial breath-hold dual-echo(in-phase and opposed-phase) T1W fast field-echo
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| TR/TE1/TE2
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| <math>106/1.15/2.3</math>
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|-
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| stile="width:100px;text-align:left;"| slice thickness /gap
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| stile="width:100px;text-align:left;"| <math>7/1</math> mm
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|-
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| FOV
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| <math>400\times 322</math> cm
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|-
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| style="border-bottom: 0pt solid black;"| matrix size
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| style="border-bottom: 0pt solid black;"| <math>244\times 181</math>
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|-
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| style="height:20px"|
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|-
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|  rowspan='4' style="width:400px;text-align:left;padding-right:15px;border-bottom: 0pt solid black;"|Fat-suppressed dynamic 3D volumetric interpolated breath-hold T1W sequence (before /after injection)
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| TR/TE1/TE2
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| <math>3.6/1.32/2.3</math>
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|-
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| slice thickness /gap
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| <math>5/-2.5</math> mm
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|-
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| FOV
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| <math>320\times 427</math> cm
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|-
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| style="border-bottom: 0pt solid black;"| matrix size
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| style="border-bottom: 0pt solid black;"| <math>200\times 250</math>
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|-
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| style="height:20px"|
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|-
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|  rowspan='2' style="border-bottom: 0pt solid black;"|Injection of Gd-EOB-DTPA
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| Primovist/ flow rate
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| <math>0.1</math> mL/kg  <math>1</math> mL/s
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|-
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| style="border-bottom: 0pt solid black;"| saline/ flow rate
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| style="border-bottom: 0pt solid black;"| <math>20</math>ml  <math>2</math>ml/s
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|-
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| style="height:20px"|
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|-
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|  rowspan='5' style="width:400px;text-align:left;padding-right:15px;border-bottom: 0pt solid black;"|The dynamic enhanced sequence
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| pre-enhanced phase
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| -
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|-
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| arterial phase
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| <math>20</math>s after injection
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|-
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| portal  venous phase
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| <math>55</math> s after injection
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|-
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| equilibrium phase
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| <math>90</math>s after injection
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|-
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| style="border-bottom: 0pt solid black;"| delayed phase
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| style="border-bottom: 0pt solid black;"| <math>180</math>s after injection
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|-
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| style="height:20px"|
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|-
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|  style="border-bottom: 2pt solid black;"|Hepatobiliary phase images
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|  style="border-bottom: 2pt solid black;"|-
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|  style="border-bottom: 2pt solid black;"|<math>20</math>min after injection
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|-
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| colspan='3' style="text-aling:left;"| TR, repetition time; TE, echo time; FOV, field of view; RT, respiratory-triggered
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|}
  
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Graphics may be inserted directly in the document and positioned as they should appear in the final manuscript.
 
  
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Number the figures according to their sequence in the text. Ensure that each illustration has a caption. A caption should comprise a brief title. Keep text in the illustrations themselves to a minimum but explain all symbols and abbreviations used. Try to keep the resolution of the figures to a minimum of 300 dpi. If a finer resolution is required, the figure can be inserted as supplementary material
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For each patient, the tumor ROI was evaluated by the radiologist (15-year abdominal diagnosis experience). Dynamic enhancement MRI data was analyzed using MATLAB (MathWorks, Natick, MA) with an in-house software package. The peritumoural region (dilated distance was analysed as dilated pixel counts multiplied by pixel size) of the labeled ROI was dilated at a radius of 1 cm automatically by topology algorithm. The peritumoural region in this paper is 8 pixels thick. Then, all the pixels on the peritumoural region were used for kinetic analysis.
  
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For tabular summations that do not deserve to be presented as a table, lists are often used. Lists may be either numbered or bulleted. Below you see examples of both.
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ROI Topology Algorithm has follow steps:
  
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1. The first entry in this list
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'''Step 1''': Search the ROI pixels (value=1);
  
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2. The second entry
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'''Step 2''': if the product of adjacent pixels value equal 0, that means the pixel is an edge;
  
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2.1. A subentry
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'''Step 3''': let the adjacent pixel equal 1 to enlarge the ROI with 1 pixel thick;
  
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3. The last entry
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So, one loop can make the ROI enlarge 1 pixel thick, and in our research the algorithm need loop 8 times.
  
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* A bulleted list item
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For the kinetic analysis, the signal intensity curve (<math>S(t)</math>) were utilized from the dynamic enhancement MRI data [15].  For each patient, average peritumoural region enhancement curve was used to calculate the kinetic parameters. The baseline signal intensity value (<math>S_0</math>) of each pixel was the signal intensity (<math>S(t)</math>) of the precontrast time point. Three quantitative kinetic parameters, namely initial enhancement (<math>E_1</math>), peak enhancement (<math>E_{\rm peak}</math>), and enhancement ratio (<math>E_R</math>) were calculated using <math>S(t)</math> as follows [16]:
  
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* Another one
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{| class="formulaSCP" style="width: 100%; text-align: center
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|-
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|
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{| style="text-align: centermargin:auto;width: 100%;"
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|-
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| style="text-align: center| <math>E_1=(S_1 -S_0/ S_0</math>,
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|}
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|  style="text-align: right;vertical-align: top;width: 5px;text-align: right;white-space: nowrap;"|(1)
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|}
  
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You may choose to number equations for easy referencing. In that case they must be numbered consecutively with Arabic numerals in parentheses on the right hand side of the page. Below is an example of formulae that should be referenced as eq. (1].
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{| class="formulaSCP" style="width: 100%; text-align: center
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|-
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|
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{| style="text-align: centermargin:auto;width: 100%;"
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|-
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| style="text-align: center| <math>E_{\rm peak}=(S_{\rm peak} -S_0)/ S_0</math>,
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|}
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|  style="text-align: right;vertical-align: top;width: 5px;text-align: right;white-space: nowrap;"|(2)
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|}
  
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{| class="formulaSCP" style="width: 100%; text-align: center
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|-
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|
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{| style="text-align: centermargin:auto;width: 100%;"
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|-
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| style="text-align: center| <math>E_R=(S_1-S_0)/ (S_{\rm last}-S_0)</math>,
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|}
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|  style="text-align: right;vertical-align: top;width: 5px;text-align: right;white-space: nowrap;"|(3)
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|}
  
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2.4 Supplementary material
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where <math>S_0</math>, <math>S_1</math>, <math>S_{\rm peak}</math>, and <math>S_{\rm last}</math> are the signal intensity at the baseline, the 1st enhancement time point, the peak point and the last time point, respectively. The detailed data analysis steps are shown in Figure 1 flowchart.
  
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Supplementary material can be inserted to support and enhance your article. This includes video material, animation sequences, background datasets, computational models, sound clips and more. In order to ensure that your material is directly usable, please provide the files with a preferred maximum size of 50 MB. Please supply a concise and descriptive caption for each file. -->==
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{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: auto;max-width: auto;"
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|-
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|style="padding:10px;"| [[Image:Draft_zhou_568611866-image1.png|526px]]
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|- style="text-align: center; font-size: 75%;"
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| colspan="1" style="padding-bottom:10px;"| '''Figure 1'''. The flowchart of detailed steps used in the kinetic analysis
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|}
  
  
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Student t-tests were performed to examine whether there were significant differences between MVI positive and negative groups for calculated quantitative parameters. Receiver operating characteristics (ROC) analysis was performed to evaluate whether calculated parameters could be used to distinguish between MVI positive and negative groups. A p-value less than 0.05 was considered as statistically significant.
  
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==3. Results==
  
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==3 Bibliography<!--
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===3.1. Patients===
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Citations in text will follow a citation-sequence system (i.e. sources are numbered by order of reference so that the first reference cited in the document is [1], the second [2], and so on) with the number of the reference in square brackets. Once a source has been cited, the same number is used in all subsequent references. If the numbers are not in a continuous sequence, use commas (with no spaces) between numbers. If you have more than two numbers in a continuous sequence, use the first and last number of the sequence joined by a hyphen
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You should ensure that all references are cited in the text and that the reference list. References should preferably refer to documents published in Scipedia. Unpublished results should not be included in the reference list, but can be mentioned in the text. The reference data must be updated once publication is ready. Complete bibliographic information for all cited references must be given following the standards in the field (IEEE and ISO 690 standards are recommended). If possible, a hyperlink to the referenced publication should be given. See examples for Scipedia’s articles [1], other publication articles [2], books [3], book chapter [4], conference proceedings [5], and online documents [6], shown in references section below. -->==
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For all 61 patients, they were diagnosed as a MVI positive group (<math> n = 19</math>) if the invasion of tumor cells within a vascular space lined by endothelium that is visible only on microscopy, and otherwise as an MVI negative group (<math>n=42</math>).
  
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Figure 2 shows the peritumoral region and the corresponding signal intensity curve <math>S(t)</math> of a MVI positive patient and a negative one for example. The curve demonstrated that there was clearly higher enhancement for the MVI positive group compared to the MVI negative group.
  
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{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 60%;"
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|-
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|  [[Image:Draft_zhou_568611866-image2.png|580px]]
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|- style="text-align: center; font-size: 75%;"
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| colspan="1" style="padding-bottom:10px;padding-left:10px;padding-right:10px;"| '''Figure 2'''. The peritumoural region and the corresponding signal intensity curve <math>S(t)</math> of MVI positive patient, male, 47 years old (a and b) and negative patient, male, 44 years old (c and d). The curve demonstrated that there was clearly faster enhancement (E1) and washout (ER) for the MVI positive group compared to the MVI negative group
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|}
  
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===3.2. Kinetic analysis===
  
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==4 Acknowledgments<!-- Acknowledgments should be inserted at the end of the document, before the references section. -->==
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Figure 3 shows boxplots of kinetic parameters for both MVI positive and negative groups: (a) initial enhancement <math>E_1</math>, (b) peak enhancement <math>E_{\rm peak}</math>, and (c) enhancement ratio <math>E_R</math>. Average (<math>\pm SD</math>) <math>E_1</math> is <math>0.36\pm 0.12</math> and  <math>0.46\pm 0.09</math>, <math>E_{\rm peak}</math> is  <math>0.78\pm 0.24</math> and <math>0.74\pm 0.18</math>, and <math>E_R</math> is <math>0.42\pm 0.20</math> and <math>0.56\pm 0.17</math> for negative and positive group, respectively. Statistical analysis showed that average <math>E_1</math> and <math>E_R</math> for the positive group were significantly higher (<math>p < 0.05</math>) than the negative group.The <math>E_{\rm peak}</math> for positive group was slightly lower (<math>p=0.49</math>) than the negative group.
  
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{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 60%;"
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|-
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|style="padding:10px;"| [[Image:Draft_zhou_568611866-image3.png|450px]]
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|- style="text-align: center; font-size: 75%;"
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| colspan="1" style="padding-bottom:10px;padding-left:10px;padding-right:10px;"| '''Figure 3'''. Boxplots of parameters for both negative and positive groups: (a) <math>E_1</math> and (b) <math>E_R</math>. The square (□) indicates mean of the data. The <math>E_1</math> value for negative group and positive group were <math>0.36\pm 0.12</math> and <math>0.46\pm 0.09</math>, the <math>E_R</math> value <math>0.42\pm 0.20</math> and <math>0.56\pm 0.17</math> for negative and positive group, respectively. Statistical analysis showed that average <math>E_1</math> and <math>E_R</math> for the positive group were significantly higher (<math>p < 0.05</math>) than the negative group
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|}
  
  
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Finally, Figure 4 shows the ROC analysis results for the parameters <math>E_1</math> and <math>E_R</math> obtained from kinetic analysis parameters. By selecting the Youden index cut-off point, the calculated sensitivity is 0.57 and 0.52; and specificity is 0.75 and 0.75 for the <math>E_1</math> and <math>E_R</math>, respectively. The corresponding cut-off value is 0.44 and 0.58 for the <math>E_1</math> and <math>E_R</math>, respectively.
  
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==5 References<!--[1] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Article code. Available: http://www.scipedia.com/ucode.
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{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 60%;"
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|-
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|style="padding:10px;"| [[Image:Draft_zhou_568611866-image4.png|300px]]
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|- style="text-align: center; font-size: 75%;"
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| colspan="1" style="padding-bottom:10px;padding-left:10px;padding-right:10px;"| '''Figure 4'''. The receiver operating characteristics (ROC) analysis results between MVI negative and MVI positive group for the parameters <math>E_1</math> and <math>E_R</math>. The same color dot on the same color line is indicating the Youden index cut-off point
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|}
  
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[2] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Volume number, first page-last page.
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==4. Discussion==
  
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[3] Author, C. (Year). Title of work: Subtitle (edition.). Volume(s). Place of publication: Publisher.
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<span id='OLE_LINK4'></span><span id='OLE_LINK5'></span>
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We investigated the feasibility of using dynamic enhancement MRI to directly assess the peritumoral enhancement for prediction the MVI of sHCC. The dynamic enhancement MRI included pre-enhanced phase, arterial phase, portal venous phase, equilibrium phase and delayed phase, which can help us track the blood supply. The kinetic analysis for dynamic enhancement MRI could be more sensitive to imperceptible changes. Furthermore, the enhancement could be analyzed with the physiological compartment model or mathematical model to obtain the qualitative results of slight changes in the lesion hemodynamics, so as to help doctors make accurate diagnosis. The downside of using enhancement curve is that the dynamic enhancement MRI needs to be acquired with more time points, which may add to the burden of clinical work. However, dynamic enhanced liver examination has become a routine method for clinical diagnosis. We believe that kinetic analysis can bring more reliable information for clinical diagnosis.
  
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[4] Author of Part, D. (Year). Title of chapter or part. In A. Editor & B. Editor (Eds.), Title: Subtitle of book (edition, inclusive page numbers). Place of publication: Publisher.
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Our results demonstrated that the kinetic analysis of peritumoral enhancement measured from the dynamic enhancement MRI could be useful for quantitatively evaluating the MVI of sHCC. The parameters <math>E_1</math> and <math>R</math> are higher in the positive group than negative group, which means there are faster blood supply and faster washout in the liver parenchyma around the tumor. The feature indicates there is the same kinetic characteristic in the peritumoral region as intratumoral of HCC which increases the probability of MVI. These agreements were very encouraging to inspire more studies in the future. We believe that this is the first study to quantitatively evaluate peritumoral enhancement measured from the dynamic enhancement MRI for prediction the MVI of sHCC.
  
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[5] Author, E. (Year, Month date). Title of the article. In A. Editor, B. Editor, and C. Editor. Title of published proceedings. Paper presented at title of conference, Volume number, first page-last page. Place of publication.
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Previous studies of predicting the MVI of sHCC were mainly on tumor morphology by evaluating the smooth of tumor margin [6,17], the regularity of tumor shape [18], the completeness of tumor capsule [19]. There is a lack of relevant quantitative research.
  
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[6] Institution or author. Title of the document. Year. [Online] (Date consulted: day, month and year). Available: http://www.scipedia.com/document.pdf.  
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The tumor morphology may be not equal to the MVI of sHCC for several reasons.  First, the judgment of sHCC from morphology is limited [20,21]. Second, some image features due to individual blood supply differences may not always appear in the corresponding acquisition phase. Third, these features are mainly evaluated by the naked eye of experienced radiologist. Our quantitative analysis of dynamic enhancement MRI could be used to overcome above problems to some extent by quantifying the prediction the MVI of sHCC.
−
-->==
+
 
 +
A potential advantage of dynamic enhancement MRI would be the possibility to perform one-stop examination just before HBP image. As a routine clinical examination, dynamic enhancement data are relatively easy to obtain.
 +
 
 +
There were several limitations to this study. First, the scale of our research was relatively small, especially the positive group. Second, the study did not include comparisons of blood supply changes between different contrast agents. Third, the sHCC in the study is restricted to diameter not bigger than 3cm. The HCC with diameter between 3 and 5cm should also be predicted in the future. Finally, our developed techniques were not tested on different datasets to evaluate the sensitivity and specificity obtained in this study.
 +
 
 +
There is potential clinical application using kinetic analysis for dynamic enhancement MRI in assisting the MVI of sHCC. Quantitative analysis parameters could be used as objective measures in diagnosis. Our study would inspire more research on the prediction the MVI for the solitary sHCC patients in the future.
 +
 
 +
==Funding==
 +
 
 +
This research was funded by Harbin youth reserve talent project (2017RAQXJ102) and Harbin Medical University Cancer Hospitals Haiyan Funds (JJZD2020-17).
 +
 
 +
==Conflicts of Interest==
 +
 
 +
The authors declare no conflict of interest.
 +
 
 +
==References==
 +
<div class="left" style="font-size: 85%;">
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[1] Imai K., Yamashita Y.I., Yusa T., et al. Microvascular invasion in small-sized hepatocellular carcinoma: significance for outcomes following hepatectomy and radiofrequency ablation. Anticancer Res.,  38(2):1053-1060, 2018.
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[2] Ahn S.J., Kim J.H., Park S.J., Kim S.T., Han J.K. Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis. Abdom. Radiol., 44(2):539-548, 2019.
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[3] Yamashita Y.I., Imai K., Yusa T., et al.  Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: Predictors and optimal treatments. Ann. Gastroenterol. Surg., 2(3):197-203, 2018.
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[4] Kim K.A., Kim M.J., Jeon H.M., et al. Prediction of microvascular invasion of hepatocellular carcinoma: usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images. J Magn. Reson. Imaging, 35(3):629-34, 2012.
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[5] Ariizumi S., Kitagawa K., Kotera Y., et al. A non-smooth tumor margin in the hepatobiliary phase of gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging predicts microscopic portal vein invasion, intrahepatic metastasis, and early recurrence after hepatectomy in patients with hepatocellular carcinoma. J. Hepatobiliary Pancreat. Sci., 18(4):575-85, 2011.
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[6] An C., Rhee H., Han K., et al. Added value of smooth hypointense rim in the hepatobiliary phase of gadoxetic acid-enhanced MRI in identifying tumour capsule and diagnosing hepatocellular carcinoma. Eur. Radiol., 27(6):2610-2618, 2017.
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[7] Choi Y.S., Rhee H., Choi J.Y., et al. Histological characteristics of small hepatocellular carcinomas showing atypical enhancement patterns on gadoxetic acid-enhanced MR imaging. J. Magn. Reson. Imaging,  37(6):1384-1391, 2013.
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[8] Grazioli L., Olivetti L., Fugazzola C., et al. The pseudocapsule in hepatocellular carcinoma: correlation between dynamic MR imaging and pathology.  Eur. Radiol., 9(1):62-7, 1999.  
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[9] Choi J.W., Lee J.M., Kim S.J., et al.  Hepatocellular carcinoma: imaging patterns on gadoxetic acid-enhanced MR Images and their value as an imaging biomarker. Radiology, 267(3):776-86, 2013.  
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[10] Joo I., Lee J.M.  Recent Advances in the imaging diagnosis of hepatocellular carcinoma: value of gadoxetic acid-enhanced MRI.  Liver Cancer, 5(1):67-87, 2016.  
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[11] Kim H., Park M.S., Choi J.Y., et al.  Can microvessel invasion of hepatocellular carcinoma be predicted by pre-operative MRI.  Eur. Radiol., 19(7):1744-1751, 2009.
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[12] Huang M., Liao B., Xu P., et al.  Prediction of microvascular invasion in hepatocellular carcinoma: preoperative Gd-EOB-DTPA-dynamic enhanced MRI and histopathological correlation. Contrast Media Mol. Imaging, 9674565, pp. 9,  2018.
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[13] Du M., Chen L., Zhao J., et al.  Microvascular invasion (MVI) is a poorer prognostic predictor for small hepatocellular carcinoma. BMC Cancer, 14:38, 2014.
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[14] Feng L.H., Dong H., Lau W.Y., et al.  Novel microvascular invasion-based prognostic nomograms to predict survival outcomes in patients after R0 resection for hepatocellular carcinoma. J. Cancer Res. Clin. Oncol., 143(2):293-303, 2017.
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[15] Speidel Ma, Bateman C.l., Tao Y., et al.  Reduction of image noise in low tube current dynamic CT myocardial perfusion imaging using HYPR processing: a time-attenuation curve analysis. Med. Phys., 40:011904, 2013.
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[16] Zhou X.Y., Zhang D.M. et al.  Quantitative analysis of lower leg muscle enhancement measured from dynamic computed tomographic angiography for diagnosis of peripheral arterial occlusive disease. Journal of Computer Assisted Tomography, 44(1):20-25, 2020.
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[17] Chen Z.H., Zhang X.P., Wang H., et al.  Effect of microvascular invasion on the postoperative long-term prognosis of solitary small HCC: a systematic review and meta-analysis. HPB (Oxford),  21(8):935-944, 2019.
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[18] Lee S., Kim S.H., Lee J.E., Sinn D.H., Park C.K. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma.  J. Hepatol., 67(3):526-534, 2017.
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</div>

Latest revision as of 12:03, 29 April 2020

Abstract

The purpose of this study is to predict preoperatively microvascular invasion (MVI) of solitary small hepatocellular cancer (sHCC) by using the kinetic parameters analysis on dynamic enhancement magnetic resonance imaging (MRI). Patients (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): n = 61 ) with known solitary sHCC(Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): \le 3 cm) were preoperatively examined with Gd-EOB-DTPA-enhanced MRI first before hepatic resection. The arterial peritumoral enhancement measured from the dynamic enhancement-MRI was analyzed by using quantitative kinetic parameters, including initial enhancement (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 ), peak enhancement (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} ), and enhancement ratio (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R ) calculated. Correlations between quantitative kinetic parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed. Histopathological analysis of liver resection confirmed that 19 patients had sHCC with MVI and that 42 patients had sHCC without MVI. Average (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): \pm 

standard deviation) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1
is Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.36\pm 0.12
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.46\pm 0.09

, Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} 

is Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.78\pm 0.24
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.74\pm 0.18

, and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R 

is Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.42\pm 0.20
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.56\pm 0.17
for negative and positive group, respectively. Statistical analysis showed that average Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1
and ER for the positive group were significantly higher (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): p < 0.05

) than the negative group. The receiver operating characteristics (ROC) analysis between the two groups had area under the curve of 0.74 and 0.71 for Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R

, respectively. Quantitative kinetic parameters analysis for the arterial peritumoral enhancement is feasibility to the prediction and assist diagnosis of MVI in clinical practice.

Keywords: Microvascular invasion, solitary small hepatocellular cancer, quantitative analysis, kinetic parameters, dynamic enhancement magnetic resonance imaging

1. Introduction

Microvessel invasion (MVI) is a major prognostic factor in hepatocellular carcinoma (HCC) that influences the choice of treatment, but rarely can be evaluated preoperatively [1,2]. Several studies have reported that certain imaging findings on dynamic enhancement MRI are useful for predicting MVI of HCC, including tumor size [3], peritumoral enhancement [4], tumor margin [5], tumor hypointensity or peritumoral hypointensity on hepatobiliary phase (HBP) [6,7], radiological capsule on gadoxetic acid–enhanced MR imaging and so on [8].

Gd-EOB-DTPA enhanced MRI can provide more valuable information for the assessment of HCC and has been widely used in preoperative evaluation settings [9,10]. Some researches use the HBP images to describe the tumor margins in the axial and coronal hepatobiliary phase, including smooth [6], wedge-shaped peritumoral enhancement [11] and irregular circumferential peritumoral enhancement [12].

However, all of these studies are based on qualitative observations and lack the kinetic parameters analysis of arterial phase tumor peritumoral enhancement on MVI. Especially for the small hepatocellular carcinoma (sHCC) with the maximum tumor diameter Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): \le 3 cm [13], there are limited studies that have investigated MR imaging finding for predicting MVI [11]. Therefore, techniques for quantitative analysis of dynamic enhancement MRI should be developed to directly predict MVI of sHCC, which results in early recurrence after hepatic resection and poor prognosis [13].

To our knowledge this is the first study to quantitatively preoperatively predict solitary sHCC with MVI based on kinetic parameters analysis of dynamic enhancement MRI. The kinetic analysis of pre-enhanced phase, arterial phase (20 seconds), portal venous phase (55 seconds), equilibrium phase (90 seconds) and delayed phase (180 seconds) enhancement curves of dynamic enhancement MRI were used in this study.

2. Materials and methods

This study was approved by the Institutional Review Board at our institution and the requirement for informed consent was waived. Patients were recruited from February 2018 to October 2019. The dynamic enhancement MRI was performed within one week before curative hepatectomy, and there is no macroscopic vascular invasion on MRI. A total of 61 patients (average age = 56 years old; 15 females and 46 males) with known single sHCC (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): \pm 3 cm) were enrolled in this study. Tumor size, number, and capsule condition were obtained at gross specimen. The entire tumor was examined for sHCC. MVI was defined as the presence of tumor in vascular space of surrounding hepatic tissue line by endothelial cells on microscopy [14].

MRI examination was performed by using 3.0-T system (Ingenia, Philips Medical Systems, Eindhoven, and The Netherlands) in all patients. 32-channel phased-array coil was used. The scanning scale covered from the top to the lower edge of live. The protocol used consisted of the following sequences which were shown in Table 1.

Table 1. The protocol used consisted of the following sequences
Sequences Index Value
Axial fat-suppressed RT T2W single shot turbo spin echo TR/ TE Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 535/75
slice thickness /gap Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 7/1 
mm
FOV Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 350\times 392 
cm
matrix size Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 232\times 199
Coronal breath-hold T2W single shot turbo spin echo TR/TE Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 1100/80
slice thickness /gap Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 6/1 
mm
FOV Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 350\times 346 
cm
matrix size Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 292\times 253
Axial breath-hold dual-echo(in-phase and opposed-phase) T1W fast field-echo TR/TE1/TE2 Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 106/1.15/2.3
slice thickness /gap Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 7/1 
mm
FOV Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 400\times 322 
cm
matrix size Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 244\times 181
Fat-suppressed dynamic 3D volumetric interpolated breath-hold T1W sequence (before /after injection) TR/TE1/TE2 Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 3.6/1.32/2.3
slice thickness /gap Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 5/-2.5 
mm
FOV Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 320\times 427 
cm
matrix size Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 200\times 250
Injection of Gd-EOB-DTPA Primovist/ flow rate Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.1 
mL/kg   Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 1
mL/s
saline/ flow rate Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 20

ml Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 2 ml/s

The dynamic enhanced sequence pre-enhanced phase -
arterial phase Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 20

s after injection

portal venous phase Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 55 
s after injection
equilibrium phase Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 90

s after injection

delayed phase Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 180

s after injection

Hepatobiliary phase images - Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 20

min after injection

TR, repetition time; TE, echo time; FOV, field of view; RT, respiratory-triggered


For each patient, the tumor ROI was evaluated by the radiologist (15-year abdominal diagnosis experience). Dynamic enhancement MRI data was analyzed using MATLAB (MathWorks, Natick, MA) with an in-house software package. The peritumoural region (dilated distance was analysed as dilated pixel counts multiplied by pixel size) of the labeled ROI was dilated at a radius of 1 cm automatically by topology algorithm. The peritumoural region in this paper is 8 pixels thick. Then, all the pixels on the peritumoural region were used for kinetic analysis.

ROI Topology Algorithm has follow steps:

Step 1: Search the ROI pixels (value=1);

Step 2: if the product of adjacent pixels value equal 0, that means the pixel is an edge;

Step 3: let the adjacent pixel equal 1 to enlarge the ROI with 1 pixel thick;

So, one loop can make the ROI enlarge 1 pixel thick, and in our research the algorithm need loop 8 times.

For the kinetic analysis, the signal intensity curve (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S(t) ) were utilized from the dynamic enhancement MRI data [15]. For each patient, average peritumoural region enhancement curve was used to calculate the kinetic parameters. The baseline signal intensity value (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S_0 ) of each pixel was the signal intensity (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S(t) ) of the precontrast time point. Three quantitative kinetic parameters, namely initial enhancement (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 ), peak enhancement (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} ), and enhancement ratio (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R ) were calculated using Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S(t) 

as follows [16]:
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1=(S_1 -S_0/ S_0

,

(1)
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak}=(S_{\rm peak} -S_0)/ S_0

,

(2)
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R=(S_1-S_0)/ (S_{\rm last}-S_0)

,

(3)

where Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S_0 , Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S_1 , Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S_{\rm peak} , and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S_{\rm last} 

are the signal intensity at the baseline, the 1st enhancement time point, the peak point and the last time point, respectively. The detailed data analysis steps are shown in Figure 1 flowchart.
526px
Figure 1. The flowchart of detailed steps used in the kinetic analysis


Student t-tests were performed to examine whether there were significant differences between MVI positive and negative groups for calculated quantitative parameters. Receiver operating characteristics (ROC) analysis was performed to evaluate whether calculated parameters could be used to distinguish between MVI positive and negative groups. A p-value less than 0.05 was considered as statistically significant.

3. Results

3.1. Patients

For all 61 patients, they were diagnosed as a MVI positive group (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): n = 19 ) if the invasion of tumor cells within a vascular space lined by endothelium that is visible only on microscopy, and otherwise as an MVI negative group (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): n=42 ).

Figure 2 shows the peritumoral region and the corresponding signal intensity curve Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S(t) 

of a MVI positive patient and a negative one for example. The curve demonstrated that there was clearly higher enhancement for the MVI positive group compared to the MVI negative group.
580px
Figure 2. The peritumoural region and the corresponding signal intensity curve Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): S(t) 
of MVI positive patient, male, 47 years old (a and b) and negative patient, male, 44 years old (c and d). The curve demonstrated that there was clearly faster enhancement (E1) and washout (ER) for the MVI positive group compared to the MVI negative group

3.2. Kinetic analysis

Figure 3 shows boxplots of kinetic parameters for both MVI positive and negative groups: (a) initial enhancement Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 , (b) peak enhancement Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} , and (c) enhancement ratio Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R . Average (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): \pm SD ) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

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and   Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.46\pm 0.09

, Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} 

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and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.74\pm 0.18

, and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R 

is Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.42\pm 0.20
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.56\pm 0.17
for negative and positive group, respectively. Statistical analysis showed that average Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R
for the positive group were significantly higher (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): p < 0.05

) than the negative group.The Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_{\rm peak} 

for positive group was slightly lower (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): p=0.49

) than the negative group.

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Figure 3. Boxplots of parameters for both negative and positive groups: (a) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 
and (b) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R

. The square (□) indicates mean of the data. The Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

value for negative group and positive group were Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.36\pm 0.12
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.46\pm 0.09

, the Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R 

value Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.42\pm 0.20
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): 0.56\pm 0.17
for negative and positive group, respectively. Statistical analysis showed that average Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R
for the positive group were significantly higher (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): p < 0.05

) than the negative group


Finally, Figure 4 shows the ROC analysis results for the parameters Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R
obtained from kinetic analysis parameters. By selecting the Youden index cut-off point, the calculated sensitivity is 0.57 and 0.52; and specificity is 0.75 and 0.75 for the Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1
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, respectively. The corresponding cut-off value is 0.44 and 0.58 for the Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

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, respectively.

300px
Figure 4. The receiver operating characteristics (ROC) analysis results between MVI negative and MVI positive group for the parameters Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 
and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_R

. The same color dot on the same color line is indicating the Youden index cut-off point

4. Discussion

We investigated the feasibility of using dynamic enhancement MRI to directly assess the peritumoral enhancement for prediction the MVI of sHCC. The dynamic enhancement MRI included pre-enhanced phase, arterial phase, portal venous phase, equilibrium phase and delayed phase, which can help us track the blood supply. The kinetic analysis for dynamic enhancement MRI could be more sensitive to imperceptible changes. Furthermore, the enhancement could be analyzed with the physiological compartment model or mathematical model to obtain the qualitative results of slight changes in the lesion hemodynamics, so as to help doctors make accurate diagnosis. The downside of using enhancement curve is that the dynamic enhancement MRI needs to be acquired with more time points, which may add to the burden of clinical work. However, dynamic enhanced liver examination has become a routine method for clinical diagnosis. We believe that kinetic analysis can bring more reliable information for clinical diagnosis.

Our results demonstrated that the kinetic analysis of peritumoral enhancement measured from the dynamic enhancement MRI could be useful for quantitatively evaluating the MVI of sHCC. The parameters Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): E_1 

and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://test.scipedia.com:8081/localhost/v1/":): R
are higher in the positive group than negative group, which means there are faster blood supply and faster washout in the liver parenchyma around the tumor. The feature indicates there is the same kinetic characteristic in the peritumoral region as intratumoral of HCC which increases the probability of MVI. These agreements were very encouraging to inspire more studies in the future. We believe that this is the first study to quantitatively evaluate peritumoral enhancement measured from the dynamic enhancement MRI for prediction the MVI of sHCC.

Previous studies of predicting the MVI of sHCC were mainly on tumor morphology by evaluating the smooth of tumor margin [6,17], the regularity of tumor shape [18], the completeness of tumor capsule [19]. There is a lack of relevant quantitative research.

The tumor morphology may be not equal to the MVI of sHCC for several reasons. First, the judgment of sHCC from morphology is limited [20,21]. Second, some image features due to individual blood supply differences may not always appear in the corresponding acquisition phase. Third, these features are mainly evaluated by the naked eye of experienced radiologist. Our quantitative analysis of dynamic enhancement MRI could be used to overcome above problems to some extent by quantifying the prediction the MVI of sHCC.

A potential advantage of dynamic enhancement MRI would be the possibility to perform one-stop examination just before HBP image. As a routine clinical examination, dynamic enhancement data are relatively easy to obtain.

There were several limitations to this study. First, the scale of our research was relatively small, especially the positive group. Second, the study did not include comparisons of blood supply changes between different contrast agents. Third, the sHCC in the study is restricted to diameter not bigger than 3cm. The HCC with diameter between 3 and 5cm should also be predicted in the future. Finally, our developed techniques were not tested on different datasets to evaluate the sensitivity and specificity obtained in this study.

There is potential clinical application using kinetic analysis for dynamic enhancement MRI in assisting the MVI of sHCC. Quantitative analysis parameters could be used as objective measures in diagnosis. Our study would inspire more research on the prediction the MVI for the solitary sHCC patients in the future.

Funding

This research was funded by Harbin youth reserve talent project (2017RAQXJ102) and Harbin Medical University Cancer Hospitals Haiyan Funds (JJZD2020-17).

Conflicts of Interest

The authors declare no conflict of interest.

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