Gastric cancer treatment may be entering an era in which a tumour’s microscopic appearance is translated into a personalised forecast before immunotherapy begins. In a study published in the British Journal of Cancer, Hong, Hwang, Kim and colleagues describe a deep-learning-derived risk score designed to predict how patients with gastric carcinoma may respond to nivolumab, a widely used immune checkpoint inhibitor. The approach relies on digital pathology: instead of assessing biopsy slides solely through human inspection, an artificial-intelligence system analyses high-resolution tissue images and searches for patterns associated with treatment outcomes.
The study addresses one of the most persistent challenges in gastric cancer immunotherapy. Nivolumab can produce long-lasting responses in some patients, but many others gain little benefit despite receiving the same treatment. Gastric cancer is biologically diverse, meaning that two tumours appearing similar under conventional examination may behave very differently once exposed to immune-based therapy. Clinicians therefore need reliable biomarkers that can distinguish patients more likely to respond from those who may require another strategy. The researchers’ model is intended to provide an additional layer of evidence by extracting predictive information directly from tumour morphology.
At present, the best-known clinical marker for selecting patients for some immunotherapy regimens is the programmed death ligand 1, or PD-L1, combined positive score. The CPS estimates the proportion of tumour cells and immune cells within a tissue sample that express PD-L1, a protein capable of suppressing immune activity. A higher score can indicate a greater likelihood of benefit from drugs such as nivolumab, which block the interaction between PD-1 on immune cells and PD-L1 on tumour or immune cells. Yet PD-L1 is not a perfect predictor. Some patients with low or negative scores respond, while some patients with high scores do not. This limitation has encouraged researchers to look for more comprehensive biological signals.
Digital pathology offers a way to examine those signals at a scale that is difficult to achieve manually. In a typical workflow, a glass pathology slide is scanned to create a whole-slide image containing millions or even billions of pixels. Deep-learning algorithms can then evaluate the architecture of the tumour, the arrangement of malignant cells, the density and distribution of immune cells, connective tissue patterns, necrotic regions and other visual features. Many of these characteristics are subtle, spatially complex or too numerous to be consistently integrated during routine assessment. A model can process them collectively and convert the resulting information into a numerical risk score.
The researchers describe a model developed from digital pathology to forecast outcomes among patients with gastric carcinoma treated with nivolumab. Rather than depending exclusively on a single molecular marker, the system is designed to learn associations between tissue appearance and clinical course. Its output is a risk estimate that could potentially help identify patients more likely to experience a favourable outcome and those at higher risk of limited benefit. The central concept is not that artificial intelligence replaces the pathologist, but that it acts as a computational assistant capable of uncovering patterns hidden within standard diagnostic material.
This distinction is important because digital pathology uses specimens already collected as part of ordinary cancer care. In principle, a predictive model based on routine slides could be easier to implement than a test requiring a new biopsy, specialised sequencing or an expensive laboratory platform. It could also be updated to combine image-derived information with clinical variables, treatment history and established biomarkers such as PD-L1 CPS. Such integration may eventually produce a more nuanced picture of a patient’s likely response than any individual measurement can provide.
However, the promise of an AI-derived score does not automatically make it ready for clinical decisions. Deep-learning systems can learn unwanted features from the data used to train them, including differences in staining protocols, scanner hardware, hospital workflows or patient selection. A model that performs well in one institution may lose accuracy when applied to slides produced elsewhere. Researchers must therefore test these systems across independent cohorts, institutions and populations, while also examining whether the model remains reliable when tissue samples are small, damaged or contain limited tumour material. Transparent reporting and rigorous validation are essential before such tools can influence treatment choices.
The study is particularly significant because nivolumab outcomes are difficult to predict using conventional clinical information alone. Immunotherapy depends on an interaction between the cancer and the patient’s immune system, and that interaction may be reflected in the organisation of cells within the tumour microenvironment. A slide can reveal not only whether immune cells are present, but also where they are located and how they relate to malignant cells. Deep learning may be able to quantify these spatial relationships, potentially identifying an “immune context” linked to treatment sensitivity. The resulting score could complement PD-L1 testing rather than compete with it.
If validated in future studies, the model could support a more precise form of treatment planning for gastric cancer. Patients predicted to have a higher probability of benefit might proceed with nivolumab-based therapy with greater confidence, while those at higher predicted risk could be considered for clinical trials, combination approaches or alternative treatments. Such a system could also help researchers design trials by identifying biologically similar patient groups and investigating why some tumours resist immune checkpoint blockade. Nevertheless, the score should be interpreted as a probability, not a verdict. Treatment decisions would still need to account for overall health, tumour stage, previous therapies, toxicity risks and patient preferences.
The work reflects a broader transformation in oncology, in which pathology images are becoming quantitative sources of biological information rather than static illustrations attached to a diagnosis. By applying deep learning to routine tissue, the researchers aim to move gastric cancer care closer to predictive medicine, where the question is not simply what a tumour looks like, but what it is likely to do when challenged by a specific therapy. The model described by Hong and colleagues does not eliminate the uncertainty surrounding nivolumab, but it offers a potentially scalable route toward reducing it. Its ultimate value will depend on independent validation, clinical integration and proof that the predictions improve outcomes for real patients.
Subject of Research: Deep learning and digital pathology for predicting nivolumab outcomes in gastric carcinoma
Article Title: A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma
Article References: Hong, Y., Hwang, I., Kim, MJ. et al. A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma. Br J Cancer (2026). https://doi.org/10.1038/s41416-026-03590-z
Image Credits: AI Generated
DOI: 10.1038/s41416-026-03590-z
Keywords: gastric cancer, gastric carcinoma, nivolumab, immunotherapy, PD-L1, combined positive score, digital pathology, deep learning, artificial intelligence, biomarkers, precision oncology
