External validation of precisebreast, a digital prognostic test for predicting breast cancer recurrence, in an early-stage cohort from the Netherlands

Abstract Background Current clinical guidelines recommend gene expression profiling to guide treatment in early-stage breast cancer. PreciseBreast (PDxBR) is a digital prognostic tool that integrates artificial intelligence (AI)-derived features from hematoxylin and eosin (H&E) slides with clinicopathologic data to predict recurrence risk. This study externally validated PDxBR in an independent cohort and compared its performance to other risk models. Methods We retrospectively analyzed PDxBR in a cohort of 739 patients with early-stage hormone receptor-positive, HER2-negative breast cancer (median follow-up of 8.8 years). For each case, one H&E-stained slide was digitized and analyzed to generate recurrence risk scores using the full PDxBR model, as well as image-only and clinical-only variants. A subset of patients who underwent MammaPrint testing was also evaluated. Model performance was assessed by AUC/C-index, hazard ratios, sensitivity, specificity, and negative and positive predictive values (NPV and PPV, respectively). Results PDxBR showed prognostic accuracy in this external cohort (AUC/C-index 0.71, 95% CI: 0.66–0.75). Applying the PDxBR threshold (≥ 58 versus < 58) yielded a hazard ratio of 3.05 (95% CI: 2.1–4.4, p < 0.001), sensitivity 0.70, specificity 0.59, NPV 0.90, and PPV 0.27. PDxBR outperformed the modified Adjuvant! Online clinical model (MINDACT model, p < 0.00001) and effectively reclassified grade 2 tumors into distinct risk groups. Conclusions PDxBR demonstrated robust prognostic performance in an independent cohort, supporting its potential as a scalable, reproducible alternative to genomic assays for individualized risk stratification in early-stage breast cancer.