Abstract 73: Accurate prediction of microsatellite instability-high gastric cancer from H&E-stained whole slide images.

Abstract Background: While prognosis is poor for patients with gastric cancer (GC), those with microsatellite instability-high disease (MSI-H) respond well to checkpoint inhibition. Next-generation sequencing approaches for MSI-H detection are complicated by cost, turnaround time, and accessibility. Artificial intelligence (AI)-powered pathology has the potential to improve MSI-H detection. Methods: Hematoxylin and eosin (H N=316) from TCGA were used, and ground truth MSI-H status was determined as described 1. A model, utilizing an additive multiple instance learning (aMIL) framework 2 and embeddings from PLUTO v3.1* 3 (PathAI, Boston, MA), a pathology foundation model, was trained to predict slide-level MSI-H status using 5-fold cross-validation. Model predictions were compared to ground truth labels using area under the receiver operating curve (AUROC) analysis. Results: Model performance results are summarized in Table 1. The aMIL model achieved a mean AUROC of 0.86 (range: 0.81-0.89). These model predictions were highly accurate and consistent across folds, suggesting that the model is highly robust for predicting MSI-H status. Conclusions: Here, we describe an AI pathology model that consistently and accurately identifies MSI-H GC from H1:PO.17.00073. 2) arXiv:2206.01794 3) arXiv:2405.07905 *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Shima Nofallah, Jake Conway, Jacqueline Brosnan-Cashman, Syed Ashar Javed, Bahar Rahsepar. Accurate prediction of microsatellite instability-high gastric cancer from H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 73.