Lauren subtype classification in gastric cancer using deep learning on real-world H&E images
Presenter: Akul Singhania, BS;PhD Session: Digital Pathology 2 Time: 4/20/2026 9:00:00 AM → 4/20/2026 12:00:00 PM
Authors
Akul Singhania , Qiyuan Hu , Riccardo Miotto , Justin Guinney , Radia M. Johnson Tempus AI, Inc., Chicago, IL
Abstract
Introduction: Gastric cancer (GC) is a heterogeneous disease, with Lauren classification providing a framework to assign diffuse and intestinal subtypes, informing prognosis and therapy. Traditional subtype assignment relies on pathologist review of hematoxylin and eosin (H&E)-stained slides, leading to inter-observer variability and scalability challenges. We developed a deep learning classifier to automate Lauren subtype assignment on real-world H&E images. Methods: We analyzed de-identified H&E-stained whole slide images (WSI) from biopsies and resections of 2974 GC patients (3160 samples) from the Tempus real-world database. Samples with pathologist-assigned labels (n=399 diffuse; n=238 intestinal) were used for classifier training. WSI were preprocessed into tissue tiles and tile embeddings were extracted using the H-optimus-0 pathology foundation model. An additive attention-based multiple instance learning model was trained with cross-entropy loss weighted by class prevalence. Data were split 80/20 for development/holdout, with 5-fold cross-validation for model tuning and selection, and ensembled predictions from 5 cross-validation models were used to assign subtypes. An operating point was selected for ~90% positive predictive value (PPV) on the holdout set for each class. Real-world overall survival (rwOS; time from first-line therapy to death) was assessed in patients with available data (31%). Results: The model achieved a robust performance (AUC 0.93, 95% CI: 0.88-0.98) on the holdout set. With PPV-optimized thresholds, previously unlabeled samples (n=2523) were assigned by the model as diffuse (n=1321, 52.4%), intestinal (n=749, 29.7%), or indeterminate (n=453,17.95%). For pathologist-assigned samples, diffuse cases had worse median OS (13.3 months, 95% CI: 11.5-15.8) than intestinal (22 months, 95% CI: 15.1-29.8; p=6.2e-4). For classifier-assigned samples, diffuse cases had a shorter median OS (12.6 months, 95% CI: 10.8-15.7) than intestinal (15.3 months, 95% CI: 12.2-17.3; p=0.95). CDH1 mutations were found in 30.3% of pathologist-labeled and 23.7% of classifier-assigned diffuse tumors, but were rare in intestinal tumors (1.3%, 1.1%). RHOA mutations were present in 8.5% of pathologist-labeled and 8.3% of classifier-assigned diffuse tumors, versus 2.5% in intestinal tumors for both groups. Other histologies predominantly aligned with model predictions: signet ring cell carcinoma was predicted diffuse, while tubular, papillary, and mucinous adenocarcinomas were predicted intestinal. Conclusions: This deep learning classifier can accurately assign Lauren subtypes in GC from real-world H&E-stained WSI, reducing manual review and variability. Model predictions align with known clinical and molecular differences between subtypes, supporting standardization of Lauren classification and enabling large-scale studies of GC.
Disclosure
A. Singhania, Tempus AI Employment, Stock. Q. Hu, Tempus AI Employment, Stock. R. Miotto, Tempus AI Employment, Stock, Patent. J. Guinney, Tempus AI Employment, Stock, Patent. R. M. Johnson, Tempus AI Employment, Stock, Patent. Gilead Sciences Employment, Stock.
Cited in
Control: 1378 · Presentation Id: 3101 · Meeting 21436