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.
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;
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;
The study population consists of patients histopathologically diagnosed with hepatocellular carcinoma who underwent curative R0 surgical resection. Eligible participants are aged 18-75 years with complete and high-quality preoperative and postoperative contrast-enhanced CT/MRI images, qualified postoperative H\&E-stained FFPE pathological slides, and complete clinicopathological and follow-up data. Patients with prior non-collaborative-center antitumor treatment, concurrent other malignancies, positive surgical margins, or severely unqualified imaging and pathological specimens are excluded.
Cohort
Cross-sectional
AI adjuvant therapy
Other interventionThis is a purely observational study involving no clinical intervention. The multimodal AI model analyzes patients' preoperative imaging, postoperative digital pathological slides, and clinical indicators to predict HCC postoperative recurrence risk and optimal adjuvant therapy regimens. All AI outputs are used only for research recording and outcome comparison. No model predictions will affect physicians' real clinical decisions, treatment plans, or patient management throughout the study.
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.
recurrence rate
rate of recurrence after the surgery
overall survival
Overall Survival (OS) refers to the length of time from the completion of curative hepatectomy for hepatocellular carcinoma (such as partial hepatectomy or liver transplantation) until the patient's death from any cause.
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