Liver CancerAIM-LCAPNCT07658586
AI Multimodal Model for Liver Cancer Diagnosis and Prognosis
An observational study of Liver Cancer and Hepatocellular Carcinoma, run by Guangxi Medical University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Liver Cancer, Hepatocellular Carcinoma, Intrahepatic Cholangiocarcinoma (Icc)
- How many people
- 600 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2025to 1 Dec 2028
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: (1) Key clinical, imaging, or pathological data severely missing or incomplete; (2) Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis; (3) Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery; (4) Concurrent other malignant tumors; (5) Lost to follow-up or follow-up data cannot meet endpoint determination requirements.
Who may be able to join
- -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
Show all 12Show fewer
- 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)
Who cannot join
- · 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
Trial locations and contacts
1 site in 1 country.
China1 site
Guangxi Medical University First Affiliated Hospital
Nanning, Guangxi
About this study
Official title: A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data
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.
- Medical subject headings
- Liver Neoplasms, Carcinoma, Hepatocellular, Cholangiocarcinoma, Cirrhosis, Familial, with Pulmonary Hypertension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Diagnostic | 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. | |
| Prognostic | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification
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.
Measured: At the time of initial diagnosis
Prognostic Performance of the Multimodal AI Model for Postoperative Survival Prediction
The prognostic performance of the multimodal AI model in predicting postoperative progression-free survival (PFS) and overall survival (OS) in patients with pathologically confirmed liver cancer who underwent radical hepatectomy. Performance metric includes the concordance index (C-index). Calibration curves are also assessed.
Measured: minimum follow-up of 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2026
- Study start1 Dec 2025
- Primary completion1 Dec 2028 (estimated)
- Study completion1 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Liver CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangxi Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fubo Wang, Guangxi Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07658586
- Sponsor's protocol ID
- KY20260007
- Acronym
- AIM-LCAP
- Status last verified
- Jun 2026
- First submitted
- 14 Jun 2026
- Last updated
- 1 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Di 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.
PubMed 36320613Xu 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.
PubMed 37167049Schmauch 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.
PubMed 38292073Ji 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.
PubMed 31934830Peng 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.
PubMed 31332558Castaldo 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.
PubMed 34209197Wang 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.
PubMed 38993637He Z, She X, Liu Z, Gao X, Lu LU, Huang J, Lu C, Lin Y, Liang R, Ye J. Advances in post-operative prognostic models for hepatocellular carcinoma. J Zhejiang Univ Sci B. 2023 Mar 15;24(3):191-206. doi: 10.1631/jzus.B2200067.
PubMed 36915996Herden U, Schoening W, Pratschke J, Manekeller S, Paul A, Linke R, Lorf T, Lehner F, Braun F, Stippel DL, Sucher R, Schmidt H, Strassburg CP, Guba M, van Rosmalen M, Rogiers X, Samuel U, Schon GM, Nashan B. Accuracy of Pretransplant Imaging Diagnostic for Hepatocellular Carcinoma: A Retrospective German Multicenter Study. Can J Gastroenterol Hepatol. 2019 Mar 5;2019:8747438. doi: 10.1155/2019/8747438. eCollection 2019.
PubMed 30949459Saito R, Amemiya H, Hosomura N, Kawaida H, Maruyama S, Shimizu H, Furuya S, Akaike H, Kawaguchi Y, Sudo M, Inoue S, Kono H, Ichikawa D. Prognostic Significance of Treatment Strategies for the Recurrent Hepatocellular Carcinomas After Radical Resection. In Vivo. 2020 May-Jun;34(3):1265-1270. doi: 10.21873/invivo.11900.
PubMed 32354917Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4.
PubMed 38572751