RSC4All as a machine learning nomogram to predict RSClin™ results in HR+/HER2- node-negative early breast cancer.

ABSTRACT:

Background

RSClin™ combines clinicopathological (CP) and genomic data to refine predictions of distant recurrence (DR) risk and chemotherapy (CT) benefit in node-negative (N0) hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer (eBC). Available in the United States, RSClin™ is inaccessible in Europe due to regulatory constraints. We developed and validated RSC4All, a machine learning (ML) nomogram replicating RSClin™ predictions and evaluating clinical impact by comparing DR risk and CT benefit estimates across recurrence score (RS), RSClin™, and RSC4All.

Materials and methods

We retrospectively identified 290 HR-positive/HER2-negative eBC with both 21-gene RS and RSClin™ estimation at three European centers (developmen

SUBMITTER: Jacobs F 

PROVIDER: S-EPMC12538438 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature