Clinical trial · Observational
AI Multimodal Model for Liver Cancer Diagnosis and Prognosis
A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to develop a comprehensive artificial intelligence model system integrating preoperative multimodal data (CT/MRI imaging, clinical laboratory data, and radiology report text) to achieve two core objectives. First, to develop a multimodal fusion diagnostic model for non-invasive and accurate preoperative differentiation of liver cancer subtypes, including distinguishing benign from malignant lesions and differentiating hepatocellular carcinoma from intrahepatic cholangiocarcinoma. Second, to develop a prognostic prediction model for patients with confirmed liver cancer undergoing radical surgery to assess postoperative progression-free survival and overall survival. This is a multicenter retrospective cohort study with an anticipated sample size of ≥600 patients. Model performance will be evaluated using AUC, accuracy, sensitivity, specificity, C-index, and calibration curves. Subgroup analysis will be conducted based on whether patients received neoadjuvant therapy.
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 |
|---|---|---|---|
| Hepatocellular Carcinoma | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.98 |
| Intrahepatic Cholangiocarcinoma (Icc) | Intrahepatic Cholangiocarcinoma | ONTOLOGY_EXACT | 0.85 |
| Liver Cancer | Malignant Liver Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Diagnostic
- description
- Diagnostic Model Cohort: Patients with suspected liver space-occupying lesions who underwent preoperative contrast-enhanced CT or MRI and have definite pathological diagnosis (surgical or biopsy) as gold standard.
- label
- Prognostic
- description
- Prognostic Prediction Model Cohort: Patients selected from the diagnostic cohort who were pathologically diagnosed with liver cancer, received radical hepatectomy, and have complete postoperative follow-up data (minimum 24 months) to determine progression-free survival and overall survival endpoints.
Primary outcomes (2)
- measure
- Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification
- timeFrame
- At the time of initial diagnosis
- description
- The diagnostic performance of the multimodal AI model in differentiating benign from malignant liver lesions and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma, evaluated using pathology results as the gold standard. Performance metrics include area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: -Diagnostic Model Cohort: * Age ≥18 years * Underwent preoperative contrast-enhanced CT or MRI for clinically suspected liver space-occupying lesion * Have complete preoperative clinical laboratory data * Have complete original CT/MRI imaging data and radiology reports * Have definite pathological diagnosis from surgery or biopsy as gold standard Prognostic Prediction Model Cohort (selected from diagnostic cohort): * Meet all diagnostic cohort inclusion criteria * Pathologically confirmed liver cancer * Underwent radical hepatectomy * Have complete preoperative multimodal data (CT/MRI imaging, clinical laboratory data, radiology reports) * Have complete postoperative follow-up data to determine progression-free survival and overall survival endpoints and time (minimum follow-up of 24 months) Exclusion Criteria: * · Key clinical, imaging, or pathological data severely missing or incomplete * Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis * Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery * Concurrent other malignant tumors * Lost to follow-up or follow-up data cannot meet endpoint determination requirements
References
Publications (11)
- BACKGROUNDDi Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev. 2023;56(6):5261-5315. doi: 10.1007/s10462-022-10304-3. Epub 2022 Oct 26. PMID 36320613
- BACKGROUNDXu P, Zhu X, Clifton DA. Multimodal Learning With Transformers: A Survey. IEEE Trans Pattern Anal Mach Intell. 2023 Oct;45(10):12113-12132. doi: 10.1109/TPAMI.2023.3275156. Epub 2023 Sep 5. PMID 37167049
- BACKGROUNDSchmauch B, Elsoukkary SS, Moro A, Raj R, Wehrle CJ, Sasaki K, Calderaro J, Sin-Chan P, Aucejo F, Roberts DE. Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. J Pathol Inform. 2023 Dec 29;15:100360. doi: 10.1016/j.jpi.2023.100360. eCollection 2024 Dec. PMID 38292073
- BACKGROUNDJi GW, Zhu FP, Xu Q, Wang K, Wu MY, Tang WW, Li XC, Wang XH. Radiomic Features at Contrast-enhanced CT Predict Recurrence in Early Stage Hepatocellular Carcinoma: A Multi-Institutional Study. Radiology. 2020 Mar;294(3):568-579. doi: 10.1148/radiol.2020191470. Epub 2020 Jan 14. PMID 31934830
- BACKGROUNDPeng J, Kang S, Ning Z, Deng H, Shen J, Xu Y, Zhang J, Zhao W, Li X, Gong W, Huang J, Liu L. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020 Jan;30(1):413-424. doi: 10.1007/s00330-019-06318-1. Epub 2019 Jul 22. PMID 31332558
- BACKGROUNDCastaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel). 2021 Jun 30;11(7):1194. doi: 10.3390/diagnostics11071194. PMID 34209197
- BACKGROUNDWang C, Wei F, Sun X, Qiu W, Yu Y, Sun D, Zhi Y, Li J, Fan Z, Lv G, Wang G. Exploring potential predictive biomarkers through historical perspectives on the evolution of systemic therapies into the emergence of neoadjuvant therapy for the treatment of hepatocellular carcinoma. Front Oncol. 2024 Jun 27;14:1429919. doi: 10.3389/fonc.2024.1429919. eCollection 2024.