Clinical trial · Observational
Vascular Invasion Prediction Via a Multimodal Model Supports Re-staging of Early-stage Hepatocellular Carcinoma
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 29, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260929-000001
Summary
Brief summary (as posted)
Effective strategies for personalized treatment decisions for early-stage hepatocellular carcinoma (HCC) remain critically limited. Microvascular invasion (MVI) is a distinctive pathological hallmark of HCC invasiveness. Current evidence underscores that accurate preoperative prediction of MVI has the potential to support personalized selection among surgical resection, liver transplantation, and local ablation, as well as different approaches within each modality. However, despite numerous studies on MVI prediction, accuracy remains insufficient. Currently, radiomics and liquid biopsy are at the forefront of MVI prediction. Furthermore, combining both technologies may offer a more reliable prediction, but this integrated approach remains unexplored. This large-scale prospective cohort study (an observational study design). aims to develop a multimodal approach to predict MVI by integrating four data sources: clinicopathological variables, MRI-based radiomics features, MRI-based deep learning features, and MVI-related genomic alterations detected in circulating cell-free DNA (cfDNA). Individual models will first be developed for each data modality. Their predictive outputs will be calibrated into standardized risk probabilities and integrated via decision-level fusion to generate the final multimodal model. This system will be used to preoperatively stratify patients by predicted MVI risks. Based on this risk stratification, a re-staging system for early-stage HCC will be developed. Its prognostic value and clinical utility will be evaluated, with its ability to guide the selection of optimal resection margins to improve outcomes as a key illustrative example. The primary endpoints are 5-year recurrence-free survival (RFS), 5-year overall survival (OS), and recurrence patterns.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Carcinoma, Hepatocellular | Hepatocellular Carcinoma | ALIAS | 0.90 |
| Hepatocellular Cancer | Hepatocellular Carcinoma | CURATED_BROADER | 0.80 |
| Liver Cancer | Malignant Liver Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Curative-intent liver resection | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- The MVI low-risk group
- description
- We will develop a multimodal model to predict the preoperative risk of MVI in early-stage HCC patients. The model integrates four individual models: a clinicopathological model, an MRI-based radiomics model, an MRI-based deep learning image model, and a cfDNA genomic mutation model. The patient-level predicted probabilities of MVI from these models are calibrated into standardized MVI risk probabilities (ranging from 0.0 to 1.0) using Platt scaling or isotonic regression, and are then transformed to logits before being entered into an L2-regularized logistic stacking model. The fusion coefficients of the final multimodal system will be estimated exclusively from out-of-fold predictions. After all preprocessing, calibration, fusion and threshold-selection procedures have been locked, patients with a predicted MVI probability below the cutoff determined by the maximum Youden index (sensitivity+specificity-1) will be classified as low risk for MVI. Within this low-risk group, we assess t
- interventionNames
- Procedure: Curative-intent liver resection
- label
- The MVI high-risk group
- description
- We will develop a multimodal model to predict the preoperative risk of MVI in early-stage HCC patients. The model integrates four individual models: a clinicopathological model, an MRI-based radiomics model, an MRI-based deep learning image model, and a cfDNA genomic alteration model. The patient-level predicted probabilities of MVI from these models are calibrated into standardized MVI risk probabilities (ranging from 0.0 to 1.0) using Platt scaling or isotonic regression, and are then transformed to logits before being entered into an L2-regularized logistic stacking model. The fusion coefficients of the final multimodal system will be estimated exclusively from out-of-fold predictions. After all preprocessing, calibration, fusion and threshold-selection procedures have been locked, patients with a predicted MVI probability at or above the cutoff determined by the maximum Youden index (sensitivity+specificity-1) will be classified as high risk for MVI.. Within this high-risk group,
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
1. Inclusion criteria (1) Age between 18 and 75 years; (2) Histopathologically confirmed HCC; (3) Very early and early-stage HCC meeting BCLC stage 0 or A; (4) Child-Pugh class A or B liver function; (5) Undergoing curative-intent resection, defined as complete tumor removal with tumor-free resection margins, without major vascular invasion or extrahepatic distant metastasis; (6) No preoperative anti-cancer treatment prior to surgery; (7) Availability of preoperative contrast-enhanced MRI data eligible for imaging feature assessment; (8) Availability of preoperative peripheral blood sample for cfDNA sequencing (only for patients with the sequencing); (9) Availability of complete clinicopathological and follow-up data; (10) Written informed consent for research use of clinicopathological data and biological samples. 2. Exclusion criteria (1) A history of other malignancies; (2) Residual tumors identified within 2 months after surgery; (3) Serious surgical complications per the Clavien-Dindo classification.
References
Publications (0)
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