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

NCT07417800 | Not Yet Recruiting DMC

• 1 other identifier: 2025-1125
• Study Type: interventional
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

Hepatocellular Carcinoma (HCC) is a common global malignancy, ranking 6th in incidence and 3rd in mortality, causing \~480,000 annual deaths. China accounts for over 45% of global cases, bearing a heavy disease burden. Radical resection is key for long-term survival in early-stage patients, but the 5-year postoperative recurrence rate reaches 50%-70%, limiting prognosis . Postoperative adjuvant therapies like Transarterial Chemoembolization (TACE) and Tyrosine Kinase Inhibitors (TKIs, e.g., sorafenib, lenvatinib) are widely used for high-risk recurrence patients TACE is suitable for intermediate-stage HCC by embolizing tumor vessels and perfusing chemo drugs ; multitarget TKIs inhibit pathways like VEGFR/PDGFR for anti-angiogenesis and anti-proliferation, serving as standard advanced HCC treatment . However, TACE has only 50%-60% objective response rate, with some patients suffering liver damage ; TKIs extend Recurrence-Free Survival (RFS) by 3-5 months in high-risk patients but have \<20% response rate in unselected populations, and \>50% incidence of grade 3-4 adverse events (hypertension, hand-foot skin reaction, proteinuria), leading to 20% treatment discontinuation. Currently, no efficient biomarkers exist for identifying beneficiaries, so treatment decisions rely on clinical experience (tumor size, vascular invasion), resulting in poor individualization, medical resource waste, and extra patient burden. Recent studies show the Tumor Immune Microenvironment (TIME) affects TACE/TKI sensitivity . TIME features (immune cell infiltration like CD8⁺ T cells, PD-L1 expression, spatial structure) correlate with treatment response. For example, immune-inflammatory TIME (high CD8⁺ T cell density) may improve response, while immune-exempt/desert phenotypes indicate resistance . However, TIME assessment relies on high-cost, complex technologies (mIHC, spatial transcriptomics) with poor standardization, limiting clinical use. AI (especially deep learning) enables mining deep pathological info from routine HE-stained Whole Slide Imaging (WSI, generated postoperatively for all HCC patients without extra sampling). WSI's cellular/tissue details map TIME features-models like CNN/ViT can predict "HE morphology → immune status" . HE-WSI deep learning models have high accuracy in predicting MSI (AUC 0.88) in colorectal cancer 18, PD-L1 (AUC 0.80) and TMB (AUC 0.91) in non-small cell lung cancer , and HCC recurrence risk (AUC 0.82)/immune infiltration (AUC 0.78) . Yet no studies focus on "postoperative adjuvant therapy efficacy prediction" with multicenter validation. Thus, building an HCC postoperative adjuvant therapy prediction model via HE-WSI and deep learning can clarify TIME's role and overcome tech limitations. This project integrates multicenter clinicopathological data and AI to establish/validate TACE/TKI efficacy prediction models, providing a reliable tool for HCC postoperative treatment decisions.

Conditions

Hepatocellular Carcinoma (HCC); Artificial Intelligent; Adjuvant Chemoradiotherapy; TACE; Lenvatinib; Liver Surgery

Keywords

Hepatocellular Carcinoma (HCC); Artificial Intelligent; TACE; lenvatinib; Adjuvant Chemoradiotherapy

Outcome Measures

Primary Outcomes (1)

• recurrence free survival

  Recurrence-Free Survival (RFS) refers to the length of time from the completion of curative hepatectomy for hepatocellular carcinoma (such as hepatectomy or liver transplantation) until the first documented recurrence of the tumor or the patient's death from any cause, whichever occurs first.

  Up to 3 years after curative hepatectomy

Secondary Outcomes (1)

• overall survival

  Up to 5 years after curative hepatectomy

Study Arms (2)

Model-Assisted Decision-Making Group

EXPERIMENTAL

Clinicians formulate treatment plans for patients based on the optimal adjuvant therapy regimen predicted by the model.

Procedure: TACE; Procedure: TACE combined with Lenvatinib

Standard Treatment Group

ACTIVE COMPARATOR

The adjuvant treatment plan is formulated entirely by clinicians in accordance with existing guidelines and clinical experience.

Procedure: TACE; Procedure: TACE combined with Lenvatinib

Interventions

TACE PROCEDURE

Transcatheter arterial chemoembolization (TACE) serves as a promising preventive intervention for hepatocellular carcinoma (HCC), especially in high-risk populations like those with cirrhosis or recurrent small lesions. By delivering chemotherapeutic agents and embolic materials via catheters to target vessels, it inhibits tumor angiogenesis and growth. This minimally invasive approach helps reduce HCC occurrence and improve long-term prognosis, with well-managed safety profiles in clinical practice.

Model-Assisted Decision-Making Group; Standard Treatment Group

TACE combined with Lenvatinib PROCEDURE

The combination of transcatheter arterial chemoembolization (TACE) and lenvatinib is an emerging preventive strategy for hepatocellular carcinoma (HCC) in high-risk groups. TACE blocks tumor blood supply locally, while lenvatinib inhibits angiogenesis systemically. Their synergistic effect effectively suppresses potential malignant lesions, reduces recurrence risk, and enhances preventive efficacy. This minimally invasive combined regimen, with manageable safety, has shown promising prospects in improving the long-term outcomes of high-risk populations.

Model-Assisted Decision-Making Group; Standard Treatment Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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;

You may not qualify if:

• 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;

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Related Publications (8)

• Guo 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 BACKGROUND

• Vayrynen 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 BACKGROUND

• Vanguri 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 BACKGROUND

• Jia 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 BACKGROUND

• Zeng 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.

  PMID: 35143898 BACKGROUND

• da Fonseca LG, Reig M, Bruix J. Tyrosine Kinase Inhibitors and Hepatocellular Carcinoma. Clin Liver Dis. 2020 Nov;24(4):719-737. doi: 10.1016/j.cld.2020.07.012. Epub 2020 Sep 28.

  PMID: 33012455 BACKGROUND

• Nevola R, Ruocco R, Criscuolo L, Villani A, Alfano M, Beccia D, Imbriani S, Claar E, Cozzolino D, Sasso FC, Marrone A, Adinolfi LE, Rinaldi L. Predictors of early and late hepatocellular carcinoma recurrence. World J Gastroenterol. 2023 Feb 28;29(8):1243-1260. doi: 10.3748/wjg.v29.i8.1243.

  PMID: 36925456 BACKGROUND

• Hwang SY, Danpanichkul P, Agopian V, Mehta N, Parikh ND, Abou-Alfa GK, Singal AG, Yang JD. Hepatocellular carcinoma: updates on epidemiology, surveillance, diagnosis and treatment. Clin Mol Hepatol. 2025 Feb;31(Suppl):S228-S254. doi: 10.3350/cmh.2024.0824. Epub 2024 Dec 26.

  PMID: 39722614 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Interventions

lenvatinib

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

Study Officials

• ding yuan, doctor

  Second Affiliated Hospital, School of Medicine, Zhejiang University

  STUDY CHAIR

Central Study Contacts

ding yuan, doctor

CONTACT

+86 18858101960; dingyuan@zju.edu.cn

wang weilin, doctor

CONTACT

+86 13606642087; wam@zju.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 2, 2026
• First Posted: February 18, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): January 1, 2029
• Study Completion (Estimated): December 1, 2029
• Last Updated: February 18, 2026

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share