Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma - Chunyu Lin; Yong Ren; Yu Huang; Shuqi Li; Jing Zhang; Shuai Kang; Shurong Li; Changxuan You; Qinghua Cao; Fang Liu (Cancer Imaging)
Abstract Background While triplet therapy (HTI), which combines hepatic arterial infusion chemotherapy (HAIC) with tyrosine kinase inhibitors and immune checkpoint inhibitors, is widely used in the treatment of large hepatocellular carcinoma (HCC), there are few reports about its efficacy versus HAIC, and no reliable methods are available for promptly predicting HTI response. Methods This study included treatment-naïve patients with large HCCs (> 5 cm in diameter) from two centers between January 2017 and December 2022. Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were compared between the HTI and HAIC groups. To efficiently predict HTI response, available pre-treatment H&E-stained biopsy slides of HCC patients were collected to develop deep-learning models. Results Compared to group HAIC (n = 97), group HTI (n = 281) showed an ORR (54.45% vs. 21.65%), PFS (median, 10.9 vs. 4.9 months), and OS (median, 25.0 vs. 12.0 months). No significant differences in ORR were observed within the HTI group across different BCLC stages. A deep learning model, termed the Hepatocellular Carcinoma Artificial Intelligence Prediction Model (HAIM), was developed using pathological slides of HTI-treated patients (n = 194). HAIM achieved AUC scores of 0.778 (entire testing set), 0.735 (internal testing set), and 0.853 (external testing set). Conclusions Integrating TKIs and ICIs with HAIC significantly improved ORR, PFS, and OS in all stages of large HCCs. HAIM, derived from histopathological images of the biopsy, showed potential clinical aid for predicting HTI response, providing a novel tool for personalized management of HCC.