Clinical trial · Observational
Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning
- 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)
Hepatocellular carcinoma (HCC) is a high-mortality global malignancy with a heavy disease burden in China. Although curative surgical resection improves survival for early-stage HCC patients, the 5-year postoperative recurrence rate remains as high as 50%-70%. Postoperative adjuvant TACE and systemic TKIs are standard treatments for high-risk HCC, yet both therapies have prominent drawbacks, including limited response rates, unavoidable toxicities, and inconsistent clinical benefits. Current treatment decisions rely on conventional clinical and pathological features without precise biomarkers, leading to inadequate individualized therapy and wasted medical resources. Tumor immune microenvironment and multimodal imaging-pathological features critically determine HCC treatment sensitivity. Artificial intelligence and deep learning based on preoperative radiomics and postoperative H\&E whole-slide imaging (WSI) can capture hidden tumor biological characteristics and predict therapeutic responses. However, no validated multimodal AI model is available for predicting postoperative TACE and TKI treatment outcomes in HCC, lacking large-scale multicenter prospective evidence. This study aims to construct and validate a multimodal deep learning model integrating preoperative contrast-enhanced CT/MRI, postoperative WSI, pathological reports, and clinical data, to precisely identify HCC patients sensitive to postoperative adjuvant TACE or TKI therapy and optimize individualized treatment strategies. This is a hybrid retrospective-training and prospective observational multicenter study with no clinical intervention. A total of 10,000 retrospective HCC surgical patients will be enrolled to develop an AI classification model for predicting responses to four postoperative treatment strategies: surgery alone, surgery plus TACE, surgery plus TACE combined with systemic therapy, and surgery plus exclusive systemic therapy. Subsequently, 1,000 eligible postoperative HCC patients will be prospectively and consecutively enrolled from 10-15 centers. The AI model will generate adjuvant therapy predictions without interfering with real clinical decisions. Patients will be divided into prediction-consistent and prediction-inconsistent cohorts based on the match between model predictions and actual treatments. Long-term follow-up will be performed to compare prognostic outcomes and validate the model's real-world performance and stability. Key inclusion criteria: histopathologically confirmed HCC; aged 18-75 years; received R0 curative resection; available qualified H\&E-stained FFPE slides for digital scanning; complete clinical, pathological and follow-up data; high-quality preoperative contrast-enhanced CT/MRI images eligible for AI analysis. Key exclusion criteria: prior preoperative anti-tumor therapy with unavailable baseline data; concurrent other primary malignancies; non-R0 resection; unqualified pathological slides or imaging data; severe missing clinical or follow-up information.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Adjuvant Chemoradiotherapy | — | UNRESOLVED | — |
| Artificial Intelligent | — | UNRESOLVED | — |
| Hepatocellular Carcinoma (HCC) | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.85 |
| Lenvatinib | — | UNRESOLVED | — |
| Liver Surgery | — | UNRESOLVED | — |
| TACE | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI adjuvant therapy | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- Retrospective Cohort for Deep Learning Model Construction
- description
- This cohort is a large-scale retrospective observational cohort enrolled primarily for the construction, feature screening, and preliminary internal verification of the deep learning predictive model. A total of approximately 10,000 postoperative hepatocellular carcinoma patients with contrast-enhanced computed tomography (CT) / magnetic resonance imaging ,clinial data, pathological, treatment, and follow-up data will be included. All enrolled subjects received standard surgical resection for HCC and completed standardized postoperative follow-up in participating centers. No trial-related intervention is imposed on patients. Core clinical endpoints include postoperative tumor recurrence time, recurrence pattern, overall survival, and disease-free survival. All real-world data of this cohort will be used to train, optimize, and calibrate the AI model to identify high-risk recurrence populations and generate individualized postoperative adjuvant therapy prediction schemes.
- label
- Prospective Consistent Adjuvant Therapy Cohort (AI Prediction-Matched Actual Treatment)
- description
- This is a prospective observational cohort consisting of postoperative HCC patients whose clinically implemented adjuvant therapy regimens are completely consistent with the individualized adjuvant therapy schemes predicted by the validated deep learning model. All subjects undergo routine curative resection and receive standardized postoperative management in strict accordance with clinical guidelines. The AI model only provides predictive treatment recommendations without forcing or intervening clinical decision-making, and the final treatment plan is independently determined by attending physicians. This cohort mainly verifies the clinical accuracy and practical value of the AI model. Long-term follow-up will be performed to record tumor recurrence, metastasis, survival status and adverse reactions, aiming to confirm that AI-matched adjuvant therapy can effectively reduce postoperative recurrence and improve long-term prognosis of HCC patients.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Histopathologically confirmed hepatocellular carcinoma; * Aged more than 18 years; * Underwent radical resection of primary liver cancer (R0 resection); * Availability of postoperative H\&E-stained paraffin embedded tissue sections suitable for digital whole-slide imaging; * Had complete and accessible clinicopathological data and follow-up data; * Has complete and evaluable preoperative and postoperative contrast-enhanced CT or MRI imaging with standardized scanning parameters and no severe artifacts, meeting the quality requirements for radiomic and artificial intelligence analysis. Exclusion Criteria: * Significant missing clinical or follow-up data; * Concurrent primary malignancy in other organs; * Positive surgical margin (R1 or R2 resection); * Tissue sections of poor quality (e.g., severe fading, folding, damage) unsuitable for digital scanning or analysis;
References
Publications (8)
- BACKGROUNDGuo W, Li S, Qian Y, Li L, Wang F, Tong Y, Li Q, Zhu Z, Gao WQ, Liu Y. KDM6A promotes hepatocellular carcinoma progression and dictates lenvatinib efficacy by upregulating FGFR4 expression. Clin Transl Med. 2023 Oct;13(10):e1452. doi: 10.1002/ctm2.1452. PMID 37846441
- BACKGROUNDVayrynen JP, Lau MC, Haruki K, Vayrynen SA, Dias Costa A, Borowsky J, Zhao M, Fujiyoshi K, Arima K, Twombly TS, Kishikawa J, Gu S, Aminmozaffari S, Shi S, Baba Y, Akimoto N, Ugai T, Da Silva A, Song M, Wu K, Chan AT, Nishihara R, Fuchs CS, Meyerhardt JA, Giannakis M, Ogino S, Nowak JA. Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections. Clin Cancer Res. 2020 Aug 15;26(16):4326-4338. doi: 10.1158/1078-0432.CCR-20-0071. Epub 2020 May 21. PMID 32439699
- BACKGROUNDVanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, Pagano A, Araujo-Filho JAB, Geneslaw L, Rizvi H, Sosa R, Boehm KM, Yang SR, Bodd FM, Ventura K, Hollmann TJ, Ginsberg MS, Gao J; MSK MIND Consortium; Hellmann MD, Sauter JL, Shah SP. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022 Oct;3(10):1151-1164. doi: 10.1038/s43018-022-00416-8. Epub 2022 Aug 29. PMID 36038778
- BACKGROUNDJia G, He P, Dai T, Goh D, Wang J, Sun M, Wee F, Li F, Lim JCT, Hao S, Liu Y, Lim TKH, Ngo NT, Tao Q, Wang W, Umar A, Nashan B, Zhang Y, Ding C, Yeong J, Liu L, Sun C. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature. 2025 Apr;640(8060):1031-1041. doi: 10.1038/s41586-025-08668-x. Epub 2025 Mar 12. PMID 40074893
- BACKGROUNDZeng Q, Klein C, Caruso S, Maille P, Laleh NG, Sommacale D, Laurent A, Amaddeo G, Gentien D, Rapinat A, Regnault H, Charpy C, Nguyen CT, Tournigand C, Brustia R, Pawlotsky JM, Kather JN, Maiuri MC, Lomenie N, Calderaro J. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol. 2022 Jul;77(1):116-127. doi: 10.1016/j.jhep.2022.01.018. Epub 2022 Feb 7.