Clinical Research Directory

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

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.