Computer-Aided Diagnosis for Hepatocellular Carcinoma Microvascular Invasion

Recruiting

• Conditions: Hepatocellular Carcinoma (HCC); Microvascular Invasion (MVI)

• Registration Number: NCT07170345
• Lead Sponsor: Chinese Academy of Sciences

Brief Summary

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.

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 Timeline

• Status Verified: 2025-08
• Study First Submitted: 2025-08-28
• Study Start: 2025-09-01
• Study First Submitted QC: 2025-09-05
• Last Update Submitted: 2025-09-05
• Study First Posted: 2025-09-12
• Last Update Posted: 2025-09-12
• Primary Completion: 2026-09-01
• Study Completion: 2027-09-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy	Within 1 month after surgery	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.
Area Under the Receiver Operating Characteristic Curve (AUC)	Within 1 month after surgery	The AUC will be calculated by comparing CAD system predictions with the reference standard of postoperative pathological diagnosis of microvascular invasion in hepatocellular carcinoma.

Secondary Outcome Measures

Name	Time	Method
Sensitivity	Within 1 month after surgery	Sensitivity will be calculated as the proportion of true positive MVI cases correctly identified by the CAD system compared with postoperative pathology.
Specificity	Within 1 month after surgery	Specificity will be calculated as the proportion of true negative MVI cases correctly identified by the CAD system compared with postoperative pathology.
Calibration	Within 1 month after surgery	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.

Trial Locations

Locations (19)

Meng Chao Hepatobiliary Hospital of Fujian Medical University; 🇨🇳 Fuzhou, Fujian, China

Guangdong Provincial Hospital of Traditional Chinese Medicine; 🇨🇳 Guangzhou, Guangdong, China

Zhujiang Hospital; 🇨🇳 Guangzhou, Guangdong, China

Fifth Affiliated Hospital, Sun Yat-Sen University; 🇨🇳 Zhuhai, Guangdong, China

Zhuhai People's Hospital; 🇨🇳 Zhuhai, Guangdong, China

First Affiliated Hospital of Guangxi Medical University; 🇨🇳 Nanning, Guangxi, China

Guizhou Provincial People's Hospital; 🇨🇳 Guiyang, Guizhou, China

Henan Provincial People's Hospital; 🇨🇳 Zhengzhou, Henan, China

Shengjing Hospital; 🇨🇳 Shenyang, Liaoning, China

West China Hospital; 🇨🇳 Chengdu, Sichuan, China

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