Accurate prediction of microsatellite instability-high gastric cancer from H&E-stained whole slide images
Presenter: Shima Nofallah Session: Digital Pathology 1 Time: 4/19/2026 2:00:00 PM → 4/19/2026 5:00:00 PM
Authors
Shima Nofallah 1 , Jake Conway 1 , Jacqueline Brosnan-Cashman 2 , Syed Ashar Javed 1 , Bahar Rahsepar 2 1 PathAI, Boston, MA, 2 PathAI, Inc., Boston, MA
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&E)-stained GC whole slide images (WSIs; 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 H&E-stained WSIs. The application of such models to routine GC biopsies has the potential to streamline the detection of MSI-H and other molecular biomarkers. Future studies will assess histologic features associated with model attention and extend this work to other MSI-H cancer types, including colorectal and endometrial cancer. References: 1) JCO Precis Oncol. 2017;1:PO.17.00073. 2) arXiv:2206.01794 3) arXiv:2405.07905 *For Research Use Only. Not for use in diagnostic procedures. Table 1. Performance of aMIL model for prediction of MSI-H in gastric cancer. Fold aMIL Model AUROC 1 (N MSI-H =11; N total =64) 0.87 2 (N MSI-H =9; N total =63) 0.88 3 (N MSI-H =11; N total =63) 0.89 4 (N MSI-H =14; N total =63) 0.85 5 (N MSI-H =14; Nt otal =63) 0.81
Disclosure
S. Nofallah, PathAI Employment, Stock Option. J. Conway, PathAI Employment, Stock Option. J. Brosnan-Cashman, PathAI Employment, Stock Option. Keros Therapeutics Independent Contractor. S. Javed, PathAI Employment, Stock Option.
Cited in
Control: 1400 · Presentation Id: 3077 · Meeting 21436