the Study on AI-Integrated Fully Quantitative Treatment Decision and Surgical Planning System for Liver Cancer

Background Precise tumor boundary definition on preoperative imaging is often unreliable, and regional disparities in healthcare resources limit personalized decision-making. Currently, no comprehensive AI system integrates imaging, pathology, staging, decision, and surgical planning for liver cancer.

Objectives 1: Develop a pathological-tumor-boundary evaluation system based on whole-slide pathology and CT registration. 2: Build models to predict tumor boundary and satellite micro-metastasis from CT images using deep learning and radiomics. 3: Integrate staging modules (Child-Pugh, ECOG-PS, CNLC/BCLC) and generate individualized treatment recommendations. 4: Create a surgical planning platform that calculates liver remnant volume, vascular invasion metrics, anatomical variants, and performs virtual resections. 5: Validate the system in a retrospective self-controlled multi-reader multi-case study across multiple centers.

Methods Tumor boundary & micro-metastasis prediction: Register 3D whole-slide pathology of resected specimens to preoperative CT using multiplanar reconstruction; pathologists annotate tumor and satellite lesions; train deep-learning models to predict pathological boundaries and micro-metastasis regions. Validate in 100 cases with pathological CT comparison. Decision modules: Automatically compute Child-Pugh and ECOG-PS scores from labs and records; integrate with tumor metrics and PV invasion to achieve CNLC/BCLC staging and generate decision suggestions. Surgical planning: Calculate functional liver volume requirements per consensus, estimate standard liver volume (SLV), tumor-bearing segment volume, future liver remnant (FLR/SLV), flag unsafe resections; analyze vascular invasion level, length, and perfusion territory; detect portal/bile anatomical variants for injury warnings; perform virtual anatomical and non-anatomical resections with margin control and risk predictions. Clinical validation: Conduct a retrospective, fully-crossed multi-reader multi-case trial: randomized CT reading by surgeons/radiologists with and without software assistance, separated by washout periods. Evaluate primary endpoints: AFROC-AUC for lesion detection, LROC-AUC for HCC diagnosis. Secondary endpoints include sensitivity, specificity, ROC-AUC, diagnostic consistency (Kappa), size/count accuracy (<5% error), staging concordance, and reading time.

Study population Adult participants with dynamic contrast-enhanced liver CT, including HCC-positive, HCC-negative lesion-positive, and lesion-negative cases. Exclude cases with incomplete liver imaging, heavy noise, prior liver resection, or unreadable CT.

Statistical analysis Compare AUCs between AI-assisted and manual reading; non-inferiority established if lower bound of 95% CI > 0. Secondary metrics include sensitivity, specificity, consistency, reading time, and staging accuracy.