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

Purpose: 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.

Patients and methods: 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.

Results: 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.

Conclusion: 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.