Clinical Validation of RlapsRisk BC in an international multi-cohorts setting

This study assessed whether RlapsRisk BC, a digital pathology-based AI model, can provide clinically meaningful prognostic stratification in ER-positive, HER2-negative early breast cancer. The AI model combines standard histology exhibited in whole slide images of hematoxylin and eosin stained tissue sections and clinical data. The prognostic performance of RlapsRisk BC was evaluated across multiple independent patient cohorts.

Across all validation cohorts, RlapsRisk BC showed strong prognostic performance, stratifying patients into distinct low- and high-risk groups with hazard ratios from 3.93 to 9.05. At 5 years, distant recurrence ranged from 0.85%–4.7% in low-risk vs. 6.39%–34.8% in high-risk groups. This separation remained robust across subgroups, including grade 2 tumors, menopausal status, and nodal involvement. RlapsRisk BC was also an independent prognostic factor and improved performance when combined with clinical variables (age, tumor size, nodal status). It increased the c-index by 0.08, 0.19, and 0.20 across the three cohorts, with significant improvement in two.

Compared to genomic assays, RlapsRisk BC showed complementary—and sometimes superior—performance, particularly for identifying low-risk patients. At matched specificity, it achieved higher sensitivity: 0.85 vs. 0.33 (Oncotype DX) and 0.74 vs. 0.49 (EndoPredict).

RlapsRisk BC may serve as a scalable alternative or adjunct to molecular assays, supporting more personalized and accessible treatment decisions in breast cancer, particularly in settings where genomic testing is unavailable, limited, or yields intermediate-risk results.