Enabling DCIS subtyping: leveraging foundation models for robust grading and molecular biomarker scoring.

ABSTRACT: Ductal Carcinoma In Situ (DCIS) is a non-obligate precursor of invasive breast cancer. Due to a lack of reliable prognostic markers, nearly all women with DCIS undergo intensive treatment-often unnecessarily. The LORD trial addresses this by offering active surveillance to women with screen-detected, ER-positive, HER2-negative, grade 1 or 2 DCIS, aiming to reduce overtreatment. To support this, we developed a deep learning pipeline based on foundation models to predict grade, ER, and HER2 status directly from H&E-stained digitized pathology slides. Models were trained and tested on a Dutch multicenter dataset (n = 887) and externally validated on a UK dataset (n = 259). On the Dutch data, the models achieved mean AUROCs of 0.90 (ER), 0.84 (HER2), and 0.86 (grade); external validation yield

SUBMITTER: Doyle S 

PROVIDER: S-EPMC13623920 | biostudies-literature | 2026 May

REPOSITORIES: biostudies-literature