Validation of a deep learning-based AI model for breast cancer risk stratification in postmenopausal ER+/HER2-breast cancer patients

BACKGROUND: Breast cancer prognostication is crucial for treatment decisions, and the Nottingham Histologic Grade (NHG) system is widely used. However, NHG suffers from interobserver variability, and its division into three risk groups leaves the intermediate group (comprising ∼50 % of patients) overrepresented, making individualized treatment planning challenging as prognosis within this group differ widely.

OBJECTIVES: This study aimed to validate the prognostic value of Stratipath's low and high-risk categories and five risk groups and compare NHG performance with the Stratipath deep-learning-based model.

METHODS: We analyzed clinical data from 2466 postmenopausal, ER+/HER2-breast cancer patients who did not receive chemotherapy according to guidelines at that time. The NHG and Stratipath models were compared using concordance index and hazard ratios (HR) for distant recurrence (DR), with time to any recurrence (TR) and overall survival (OS) as secondary endpoints.

RESULTS: The Stratipath five-risk group model showed similar performance to the NHG-system in predicting DR (c-index 0.71 vs. 0.72). HR for DR for Stratipath risk groups 2, 3, 4, and 5 were 1.91 (95 % CI: 1.17-3.13), 2.63 (95 % CI: 1.63-4.24), 3.18 (95 % CI: 2.00-5.07), and 3.25 (95 % CI: 2.00-5.28), respectively (p < 0.0001). In the NHG 2 subgroup, Stratipath Breast retained prognostic value for DR (HR for groups 3-5 vs. group 1: 1.73-1.85; p = 0.05), with a c-index of 0.71.

CONCLUSIONS: The Stratipath AI model performs similarly to the NHG system. Further prospective validation of the clinical benefits of differentiating Stratipath risk groups 2 and 3 in treatment strategies would be valuable.