Computer-Aided Diagnosis for Hepatocellular Carcinoma Microvascular Invasion (HCC-MVI-CAD)

Development of a Computer-Aided Diagnosis System for Hepatocellular Carcinoma Microvascular Invasion Based on Preoperative Image Analysis

Hepatocellular carcinoma (HCC) is a common malignancy in China with a high mortality rate. Its early recurrence and long-term prognosis are closely associated with tumor aggressiveness. Microvascular invasion (MVI), defined as the presence of tumor cells within small branches of the portal or hepatic veins, is a key indicator of malignant biological behavior in HCC. Clinically, MVI is strongly correlated with postoperative early recurrence and serves as an important factor in determining surgical margin extension, adjuvant therapy, and postoperative management strategies.

At present, definitive diagnosis of MVI still relies on postoperative pathological examination, and stable, effective preoperative assessment methods are lacking. Although some studies have attempted to predict MVI using preoperative imaging features, their clinical translation remains limited by poor generalizability, weak interpretability, and insufficient cross-center adaptability.

This study aims to leverage multiphase preoperative CT imaging, artificial intelligence techniques, and clinical prior knowledge to develop a high-performance, generalizable, and interpretable computer-aided diagnostic system for preoperative prediction of HCC-MVI. An observational, prospective evaluation will be conducted to assess system performance and to facilitate the clinical translation of intelligent diagnostic technologies in real-world practice.

Study Overview

• Status: Recruiting
• Conditions: Hepatocellular Carcinoma (HCC); Microvascular Invasion (MVI)
• Intervention / Treatment: Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC

Detailed Description

Hepatocellular carcinoma (HCC) is a common malignancy in China with a high mortality rate. Early recurrence and long-term prognosis are closely linked to tumor aggressiveness. Microvascular invasion (MVI), defined as the presence of tumor cells within small branches of the portal or hepatic veins, is a critical marker of malignant biological behavior. Clinically, MVI is strongly associated with early postoperative recurrence and serves as an important reference for determining surgical margin extension, adjuvant treatment, and postoperative management strategies. At present, definitive diagnosis of MVI still relies on postoperative pathological examination, and reliable preoperative assessment methods are lacking. Although prior studies have attempted to predict MVI using preoperative imaging, their clinical application remains limited by poor generalizability, weak interpretability, and insufficient cross-center adaptability.

This study aims to develop a high-performance, generalizable, and interpretable computer-aided diagnostic (CAD) system for preoperative prediction of HCC-MVI using multiphase CT imaging, artificial intelligence techniques, and clinical prior knowledge. The system will be evaluated prospectively in an observational, multicenter clinical study to assess its diagnostic value and clinical applicability.

The CAD system integrates three categories of imaging features: (1) high-level representations automatically extracted by deep neural networks; (2) predefined radiomics features such as tumor morphology, texture, and intensity distributions; and (3) structured prior features derived from radiological expertise, including tumor margin blurriness and spatial relationships with adjacent portal veins. Sparse constraints and redundancy suppression mechanisms will be applied to identify stable and efficient MVI-related representations. In addition, the system adopts a spatial domain strategy covering tumor, peritumoral, and distant regions, in order to capture invasion patterns from both local morphology and microenvironmental context, thereby constructing reproducible and clinically interpretable imaging biomarkers.

To overcome the limitations of single-domain models, the system employs a multi-source heterogeneous fusion strategy that integrates morphological-textural features, dynamic enhancement patterns, and spatial graph structures. The model architecture combines convolutional neural networks (CNNs) to capture fine-grained textures, Transformer modules to model long-range dependencies, and graph neural networks (GNNs) to represent tumor-vascular topological relationships. This hybrid approach enables comprehensive understanding of both local details and global structures. Furthermore, the model incorporates uncertainty quantification and attention-like mechanisms to dynamically adjust prediction confidence and generate saliency heatmaps. These outputs are designed to enhance clinicians' interpretability and trust in the system. An interactive visualization interface will also be developed to support risk interpretation and surgical planning.

The study will conduct a prospective observational validation across multiple clinical centers, with unified inclusion/exclusion criteria and standardized data collection protocols. Model predictions will be blindly compared against postoperative pathological results. In addition to conventional metrics (accuracy, sensitivity, specificity, and AUC), the study will observationally evaluate the impact of model-based predictions on preoperative risk stratification and surgical decision-making. By testing the system across diverse patient populations, the study aims to confirm its generalizability, clinical utility, and potential for real-world translation of intelligent diagnostic technologies.

• Study Type: Observational
• Enrollment (Estimated): 400

Contacts and Locations

• Study Contact: Name: Di Dong, Ph.D.; Phone Number: +86 13811833760; Email: di.dong@ia.ac.cn
• Study Contact Backup: Name: Mengjie Fang, Ph.D.; Phone Number: +86 18500909634; Email: fangmengjie2015@ia.ac.cn

Study Locations

• China
  • Beijing, China
    • Recruiting
    • Peking Union Medical College Hospital
    • Contact: Yilei Mao
  • Beijing, China
    • Recruiting
    • Beijing Youan Hospital
    • Contact: Hongjun Li
  • Beijing, China
    • Recruiting
    • Beijing Tsinghua Changgeng Hospital
    • Contact: Zhuozhao Zheng
  • Shanghai, China
    • Recruiting
    • Eastern Hepatobiliary Surgery Hospital
    • Contact: Yabo Jiang
  • Fujian
    • Fuzhou, Fujian, China
      • Recruiting
      • Meng Chao Hepatobiliary Hospital of Fujian Medical University
      • Contact: Xiaolong Liu
  • Guangdong
    • Guangzhou, Guangdong, China
      • Recruiting
      • Guangdong Provincial Hospital of Traditional Chinese Medicine
      • Contact: Junming He
    • Guangzhou, Guangdong, China
      • Recruiting
      • Zhujiang Hospital
      • Contact: Shihua Fang
    • Zhuhai, Guangdong, China
      • Recruiting
      • Zhuhai People's Hospital
      • Contact: Sirui Fu
    • Zhuhai, Guangdong, China
      • Recruiting
      • Fifth Affiliated Hospital, Sun Yat-Sen University
      • Contact: Jian Li
  • Guangxi
    • Nanning, Guangxi, China
      • Recruiting
      • First Affiliated Hospital of Guangxi Medical University
      • Contact: Yidi Chen
  • Guizhou
    • Guiyang, Guizhou, China
      • Recruiting
      • Guizhou Provincial People's Hospital
      • Contact: Rongpin Wang
  • Henan
    • Zhengzhou, Henan, China
      • Recruiting
      • Henan Provincial People's Hospital
      • Contact: Deyu Li
  • Liaoning
    • Shenyang, Liaoning, China
      • Recruiting
      • Shengjing Hospital
      • Contact: Meng Niu
  • Sichuan
    • Chengdu, Sichuan, China
      • Recruiting
      • West China Hospital
      • Contact: Hanyu Jiang
    • Dazhou, Sichuan, China
      • Recruiting
      • Dazhou Central Hospital
      • Contact: Jie Liu
  • Yunnan
    • Kunming, Yunnan, China
      • Recruiting
      • Yunnan Cancer Hospital
      • Contact: Yong Zha
    • Kunming, Yunnan, China
      • Recruiting
      • The First People's Hospital of Yunnan Province
      • Contact: Yun Jin
    • Kunming, Yunnan, China
      • Recruiting
      • First Affiliated Hospital of Kunming Medical University
      • Contact: Bo He
  • Zhejiang
    • Wenzhou, Zhejiang, China
      • Recruiting
      • First Affiliated Hospital of Wenzhou Medical University
      • Contact: Gang Chen

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of adult patients (≥18 years) with hepatocellular carcinoma who undergo curative-intent surgical treatment, including hepatic resection or liver transplantation, at participating clinical centers. Approximately 5,000 retrospective cases and 400 prospective cases will be included. All participants will have preoperative multiphase CT imaging and postoperative pathological evaluation with documented microvascular invasion (MVI) status.

Description

Inclusion Criteria:

• Age ≥ 18 years.
• Confirmed diagnosis of hepatocellular carcinoma (HCC) according to the Chinese Clinical Practice Guidelines for Primary Liver Cancer.
• Eligible for surgical intervention (hepatic resection or liver transplantation) according to the Chinese Clinical Practice Guidelines for Cancer, including stages Ia, Ib, and IIa.
• Preoperative imaging examination performed within 1 month before surgery.
• Availability of histopathological evaluation with documented microvascular invasion (MVI) status.

Exclusion Criteria:

• History of prior antitumor treatment, including preoperative surgical intervention, transarterial chemoembolization (TACE), radiofrequency ablation (RFA), systemic therapy, or any other preoperative intervention.
• Presence of major vascular invasion, bile duct invasion/thrombosis, extrahepatic metastasis, or lymph node involvement.
• Diffuse hepatocellular carcinoma or tumor rupture with hemorrhage.
• Lack of key data required for primary analysis.
• Poor image quality that prevents reliable qualitative or radiomics analysis.

Study Plan

How is the study designed?

Number of groups / cohorts

19

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Peking Union Medical College Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
First Affiliated Hospital of Kunming Medical University; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Beijing YouAn Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Zhujiang Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Meng Chao Hepatobiliary Hospital of Fujian Medical University; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
First Affiliated Hospital of Wenzhou Medical University; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Fifth Affiliated Hospital, Sun Yat-Sen University; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Henan Provincial People's Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Guangdong Provincial Hospital of Traditional Chinese Medicine; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Shengjing Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Beijing Tsinghua Changgeng Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Yunnan Cancer Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
The First People's Hospital of Yunnan Province; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Guizhou Provincial People's Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
First Affiliated Hospital of Guangxi Medical University; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
West China Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Zhuhai People's Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Dazhou Central Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.
Eastern Hepatobiliary Surgery Hospital; patients aged 18 years and older who undergo surgical resection for hepatocellular carcinoma with available pathological evaluation of microvascular invasion. We will collect preoperative multiphase CT images, clinical characteristics, and pathological outcomes.	Diagnostic test: Computer-Aided Diagnosis System for Preoperative Prediction of MVI in HCC; This intervention is an artificial intelligence-based computer-aided diagnosis (CAD) system developed to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma using preoperative multiphase CT imaging. The system integrates deep learning, radiomics, and expert-defined imaging features to provide risk assessment and visualization of MVI prior to surgery. In this study, the CAD system will be evaluated retrospectively and prospectively in an observational manner only. The results will not influence clinical decision-making or patient management, and all treatments will follow standard of care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area Under the Receiver Operating Characteristic Curve (AUC)	The AUC will be calculated by comparing CAD system predictions with the reference standard of postoperative pathological diagnosis of microvascular invasion in hepatocellular carcinoma.	Within 1 month after surgery
Accuracy	Accuracy will be defined as the proportion of correctly classified cases (both MVI-positive and MVI-negative) by the CAD system compared with postoperative pathology.	Within 1 month after surgery

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity	Sensitivity will be calculated as the proportion of true positive MVI cases correctly identified by the CAD system compared with postoperative pathology.	Within 1 month after surgery
Specificity	Specificity will be calculated as the proportion of true negative MVI cases correctly identified by the CAD system compared with postoperative pathology.	Within 1 month after surgery
Calibration	Calibration performance will be assessed using calibration curves, Hosmer-Lemeshow goodness-of-fit tests, and Brier scores, to determine agreement between predicted probabilities and observed MVI outcomes.	Within 1 month after surgery

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Processing Time	Average computational time required for the CAD system to generate predictions and visualization outputs will be recorded to assess feasibility for integration into clinical workflow.	Within 1 month after surgery
Physician Confidence Score	Physician Confidence Score will be measured using a questionnaire that asks physicians to rate their confidence in assessing patient risk after reviewing CAD-generated probability scores and saliency maps. Responses will be collected on a 5-point Likert scale (1 = not confident at all, 2 = slightly confident, 3 = moderately confident, 4 = confident, 5 = very confident). The score will be recorded as the numerical Likert scale value selected by each physician.	Within 1 month after surgery

Collaborators and Investigators

• Sponsor: Chinese Academy of Sciences
• Collaborators: Guangdong Provincial Hospital of Traditional Chinese Medicine; Beijing YouAn Hospital; Peking Union Medical College Hospital; West China Hospital; Shengjing Hospital; Guizhou Provincial People's Hospital; Zhujiang Hospital; Henan Provincial People's Hospital; Eastern Hepatobiliary Surgery Hospital; Fifth Affiliated Hospital, Sun Yat-Sen University; First Affiliated Hospital of Wenzhou Medical University; Yunnan Cancer Hospital; ZhuHai Hospital; First Affiliated Hospital of Guangxi Medical University; The First People's Hospital of Yunnan; Meng Chao Hepatobiliary Hospital of Fujian Medical University; Beijing Tsinghua Changgeng Hospital; Dazhou Central Hospital; First Affiliated Hospital of Kunming Medical University

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2025
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2027

Study Registration Dates

• First Submitted: August 28, 2025
• First Submitted That Met QC Criteria: September 5, 2025
• First Posted (Estimated): September 12, 2025

Study Record Updates

• Last Update Posted (Estimated): September 12, 2025
• Last Update Submitted That Met QC Criteria: September 5, 2025
• Last Verified: August 1, 2025

More Information

Terms related to this study

• Keywords: Hepatocellular Carcinoma; Artificial Intelligence; Microvascular Invasion; Computer-Aided Diagnosis System; Medical Image; Multimodal Model
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Neoplasms by Histologic Type; Digestive System Neoplasms; Digestive System Diseases; Liver Diseases; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Liver Neoplasms; Carcinoma; Carcinoma, Hepatocellular
• Other Study ID Numbers: CASMI008

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No