Condition: Hepatocellular Carcinoma (HCC) · Artificial Intelligent · Adjuvant Chemoradiotherapy · Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
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 treatm…
This description comes directly from the study's public registry record.
ding yuan, doctor · +86 18858101960 · dingyuan@zju.edu.cn
wang weilin, doctor · +86 13606642087 · wam@zju.edu.cn
Always discuss trial participation with your own doctor first.
| the Second Affiliated Hospital Zhejiang University School of Medicine | Hangzhou, Zhejiang, China | Recruiting |
Get one email when the public record changes — results posted, or the study's status changes. Nothing else, ever.
We email about this public record only. Unsubscribe anytime with one click. Never medical advice.
This page is independently generated by Eichor from the public ClinicalTrials.gov record and re-synced daily. It is not the sponsor's official website unless claimed. Nothing here is medical advice; eligibility is always determined by the study team — talk to your own doctor first.
Source record: clinicaltrials.gov/study/NCT07417800