Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Study Overview
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Brief Summary
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Microvascular invasion (MVI) has been well demonstrated as an unfavorable prognostic factor for hepatocellular carcinoma (HCC), and patients with MVI have a high risk of tumor recurrence after curative hepatectomy. Currently, the diagnosis of MVI is determined on the postoperative histologic examination, which greatly limits its influence on preoperative decision making. Therefore, we constructed this prospective study to develop a machine learning-based model for preoperative prediction of MVI by extracting high-dimensional magnetic resonance (MR) image features.

Detailed Description
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Histologically-diagnosed primary HCC after curative hepatectomy. The magnetic resonance image will be imported into the imaging management software (GE healthcare Analysis-Kit software),and the tumor lesions will manually delineated by two independent radiologists and then reconstruct into three-dimensional images for feature extraction. The radiomic textural features including grayscale histogram, transform matrix, wavelet transform and filter transformation are automatically extracted by the Analysis-Kit software.The high-throughput extracted features will be then selected by the univariate analysis, and a prediction model will be developed based on machine learning algorithm in a training set in which patients were collected from a retrospective study. And in the present study, an independent validation set will be collected and used to validate the prediction accuracy of the model.

Official Title
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Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Conditions
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Hepatocellular Carcinoma

Intervention / Treatment
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* Diagnostic Test: Magnetic resonance image


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Asian patients aged 18～80 years old; HCC without macroscopic vascular invasion according to imaging findings; Child Pugh A-B stage; Receipt of preoperative Gd-EOB-DTPA enhanced MR imaging of the abdomen within one month before surgery; Histologically-diagnosed primary HCC after curative hepatectomy; Exclusion Criteria: Combined hepatocellular-cholangiocarcinoma; With extra-hepatic metastasis or macrovascular invasion; With incomplete clinical and imaging data; Non-radical resection;

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Preoperative imaging features<br>In this project, there is only one study group which comprises of patients with Hepatocellular Carcinoma (HCC) who will undergo preoperative Gd-EOB-DTPA enhanced magnetic resonance image. | Diagnostic Test: Magnetic resonance image<br>* Histologically-diagnosed primary HCC after curative hepatectomy. The magnetic resonance image will be imported into the software ,and the radiomic textural features will be automatically extracted by the Analysis-Kit software.The high-throughput extracted features will be then selected and a prediction model will be developed in the training set in which patients were collected from a retrospective study. In this project, an independent validation set will be collected and used to validate the prediction accuracy of the model.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Presence of microvascular invasion | Postoperative histologically confirmed microvascular invasion | Through patient enrollment completion ,an average of 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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magnetic resonance image features, pathological differentiation, machine learning-based model