A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma

초록

Background The PD-L1 combined positive score (CPS) is a biomarker predicting responses in gastric cancer (GC) immunotherapy. Objectives We aimed to develop a deep learning-based model to predict responses to nivolumab in GC using PD-L1 28-8 immunohistochemistry. Methods A cell-detection network was trained on 1927 patches from 88 whole-slide images to generate a computational positive cell ratio (cPCR). The predictive performance of cPCR was evaluated in an independent cohort of 147 patients treated with nivolumab plus chemotherapy. Results The overall objective response rate (ORR) was 49.66%. ORR was 56.12% in patients with PD-L1 CPS >= 5 and 36.73% in those with CPS < 5. Using cPCR, ORR was 56.03% for cPCR >= 5 and 25.81% for cPCR <5. The AUCs of CPS (0.586) and cPCR (0.601) did not differ significantly, but net reclassification analysis showed superior predictive performance for cPCR (p = 0.0269). Incorporating cPCR with tumour-stroma ratio through stepwise variable selection enabled construction of a risk-score model that improved prediction of immunotherapy benefit. Conclusions In conclusion, we developed a cPCR-based model and risk-score system that more accurately forecasts nivolumab response in GC, offering a promising tool for optimising immunotherapy selection.

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