Abstract PD11-11: Multimodal Deep Learning for Recurrence Stratification for Early-Stage Breast Cancer in Resource-Constrained Environments

Abstract Background: Oncologists currently rely on genomic assays such as Oncotype Dx, MammaPrint and EndoPredict to guide adjuvant chemotherapy decisions in early-stage breast cancer. While offering improved risk stratification, they are costly, time-consuming, and inaccessible in many settings. Whole-slide H&E images, already generated for every patient, embed complementary prognostic biology that remains under-exploited in daily practice. Advances in artificial intelligence (AI) have shown promise in predicting molecular biomarkers directly from H&E-stained histopathology slides. In this study, we validate TRINITY AI, a multimodal deep learning platform that integrates morphology, clinicopathologic variables, and AI-inferred transcriptomics from H&E slides to predict a recurrence risk score. Methods: TRINITY AI was trained on 1219 breast cancer cases from TCGA and CPTAC. A self-supervised transformer based foundation model was used to infer transcriptomic profiles from H&E whole slide image (WSI). These inferred profiles along with clinical variables (e.g. patient age, tumour size, grade, nodal status) and morphological features are embedded into a shared latent space to learn patient-level cross-modal relationships to yield a continuous risk score. External diagnostic validation employed three distinct cohorts (n = 166) with matched Oncotype Dx scores for qualified slides that underwent strict quality control and pathologist confirmation. Prognostic utility was interrogated in 1051 TCGA cases with ≥5-year distant-recurrence follow-up using multivariable Cox models (endpoint = distant recurrence-free interval [DRFI]). Results: Against the pooled external cohort, TRINITY AI classified low-risk disease with specificity 95%, negative predictive value 92%, and area-under-the-curve 0.88, outperforming clinicopathologic nomograms and approximating Oncotype Dx. In cohort-specific analysis, it achieved a negative predictive value (NPV) >87% and specificity >92%. High-risk predictions conferred a 3.80-fold increase in DRFI hazard (95 % CI 2.08-7.18) after adjustment for size, grade, nodal status, and subtype; C-index 0.698 (95% CI: 0.622-0.770). Conclusions: TRINITY AI transforms the ubiquitous H&E slide into a genomics-grade breast cancer recurrence assay that resides within the pathologist’s digital workflow. With its performance across independent cohorts, high rule-out capacity, rapid turnaround, and fractional cost make it a pragmatic solution for equitable precision oncology. Critically, it offers a new option in underdeveloped and developing countries, where patients are often forced to choose between the cost of chemotherapy and that of expensive genomic testing. Its survival performance further supports TRINITY AI as a robust, independent predictor of distant breast cancer recurrence. Citation Format: J. Shinde, A. Ulle, C. Cha Chinglemba, Y. Thoudam, T. Gupte, S. PM, G. Shafi, H. Kothavade, E. Gustafson, R. Jawale, A. Khan, R. Kolhe, K. Bloom, M. Uttarwar. Multimodal Deep Learning for Recurrence Stratification for Early-Stage Breast Cancer in Resource-Constrained Environments [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD11-11.