ArticleBritish journal of cancer2026
A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma.
Article in British journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
backgroundThe PD-L1 combined positive score (CPS) is a biomarker predicting responses in gastric cancer (GC) immunotherapy.
objectivesWe aimed to develop a deep learning-based model to predict responses to nivolumab in GC using PD-L1 28-8 immunohistochemistry.
methodsA 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.
resultsThe 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.
conclusionsIn 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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