AzTrials
AzTrials

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.

Active, not recruiting

Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record
Search all trials

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
How many people
600 participants
Where
1 siteChina
Study dates
1 Dec 2025to 1 Dec 2028
Phase
Observational
Are there results?
Not yet: the study is under way

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

Other trials in China

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 groups in NCT07658586
Study groupRoleWhat they receive
DiagnosticDiagnostic 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.
PrognosticPrognostic 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.

  1. 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

  2. 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.

  1. First posted22 Jun 2026
  2. Study start1 Dec 2025
  3. Primary completion1 Dec 2028 (estimated)
  4. Study completion1 Dec 2028 (estimated)

Resources

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)
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 36320613

  • Xu 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 37167049

  • Schmauch 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 38292073

  • Ji 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 31934830

  • Peng 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 31332558

  • Castaldo 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 34209197

  • Wang 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 38993637

  • He 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 36915996

  • Herden 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 30949459

  • Saito 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 32354917

  • Bray 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

Questions about this trial

What is this clinical trial studying?

An observational study of Liver Cancer and Hepatocellular Carcinoma, run by Guangxi Medical University at 1 site in China.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 12 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: under way, not looking for participants, as of the sponsor's last update on 1 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification (time frame: At the time of initial diagnosis). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07658586, which also lists 11 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jul 2026.

AzTrials uses cookies for analytics and advertising. You can accept or reject them; the site works either way. Privacy Policy.

Free account

Log in to keep searching

You’ve used your 3 free searches. AzTrials is free: log in or create a free account to keep searching clinical trials and save the ones you want to follow.

Create a free accountLog in