Multimodal Artificial Intelligence Model From Baseline Histopathology Adds Prognostic Information for Distant Recurrence Assessment in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer.

Abstract

Prognostic assessment in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (EBC) remains challenging, given relatively low rates of disease progression. Modern artificial intelligence (AI)-based techniques have provided advanced prognostic tools in cancer. The Artera multimodal AI (MMAI) platform, using digital histopathology and clinical data, was applied to develop and test a prognostic risk assessment algorithm in HR+/HER2- EBC. Hematoxylin and eosin (H&E) slides from pretreatment breast biopsy and surgical specimens were digitized from the WSG PlanB and ADAPT trials. Patients with available images and complete data (n = 5,259) were stratified by trial, treatment, and distant metastasis (DM) into training (development: 60%) and internal validation (holdout: 40%) cohorts. The algorithm provided prognostic DM risk scores on the basis of image data and clinical variables (age, T and N stages, and tumor size). Univariable and multivariable Fine-Gray models were used to assess performance on the test cohort; subdistribution hazard ratios (sHR) are reported per standard deviation increase of the model scores. Prespecified prognostic subgroups for analysis were defined by nodal status, menopausal status, and tumor grade. The trained MMAI score was significantly associated with risk of DM in the test cohort (sHR, 2.3 [95% CI, 2.0 to 2.8]) as a whole and across subgroups. The score remained significant (sHR, 2.2 [95% CI, 1.7 to 2.8]) after adjusting for clinical prognostic factors. The MMAI image component alone had significant prognostic value (sHR, 1.6 [95% CI, 1.3 to 1.9]) in the test cohort; it also had significant prognostic value separately within the G2 and G3 subgroups, with sHR of 1.5 per standard deviation increase, and in most of the other predefined clinical subgroups. MMAI using digital pathology from H&E slides provides enhanced prognostic quality in HR+/HER2- EBC and could help to advance personalized breast cancer management.