An observational study in Hepatocellular Carcinoma (HCC) and Microvascular Invasion (MVI), sponsored by Chinese Academy of Sciences. Recruiting at 19 sites in China. Open to participants aged 18 Years to 18 Years. Per ClinicalTrials.gov, last updated 2025-09-12.
Sponsored by Chinese Academy of Sciences · Observational
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.
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.
3,648 studies on the registry are indexed under Carcinoma, Hepatocellular; 1,061 are open to participants now.
This study's planned enrollment of 400 is above the median of 200 across 850 observational studies indexed under Carcinoma, Hepatocellular.
Browse Carcinoma, Hepatocellular studies →Chinese Academy of Sciences is the lead sponsor of 65 studies on the registry; 13 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Metastases
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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.
Time frame: 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.
Time frame: Within 1 month after surgery
Sensitivity
Sensitivity will be calculated as the proportion of true positive MVI cases correctly identified by the CAD system compared with postoperative pathology.
Time frame: 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.
Time frame: 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.
Time frame: Within 1 month after surgery
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.
Time frame: 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.
Time frame: Within 1 month after surgery
Plan to share: Undecided
No publications or documents are linked to this record.
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
Contact study teamGet an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Chinese Academy of Sciences