Abstract PS1-13-13: Clinical validation of an Artificial Intelligence digital pathology-based prognostic test to predict risk of recurrence using biopsy specimens from patients with invasive breast cancer

Genomic testing is the standard for guiding adjuvant treatment for patients with early-stage HR+/HER2-IBC. Prognostic testing strategies using the biopsy specimen are needed to individualize patient management in the neoadjuvant and possibly adjuvant setting. The current objective was to clinically validate the PreciseBreast Biopsy test (PDxBRBx) which includes the patient’s age and morphologic features, derived from a standard H&E stained IBC biopsy slide, to predict recurrence risk earlier in the treatment planning and management process. Methods 1788 patients (pts) with IBC from the Mount Sinai Health System were identified (2004-2016) with median follow-up of 6 years (yrs). 60%-40% balanced training (surgical) and validation (biopsy) cohorts were generated. The PreciseBreast Biopsy test was performed using H&E stained slides that were imaged (40X) with a Philips Ultra-Fast scanner. Images were deconstructed to morphologic analytes using an AI-enabled platform to quantify tumor cell and tissue architectural features. Age is the only clinical factor available at the time of diagnostic biopsy and is algorithmically combined with 8 morphologic features (AI-grade model) that stratifies pts into low or high risk of recurrence. Risk stratification for invasive breast cancer free survival (IBCFS) was assessed by concordance index (C-index), area under the curve (AUC), Kaplan-Meier analysis, hazards ratios (HR), sensitivity (Se), specificity (Sp), negative predictive value (NPV), and positive predictive value (PPV). Matched excisional specimens from the biopsy test cohort were evaluated with the PreciseBreast excision test for risk score correlation using Cohen’s Kappa and Odds Ratio. Results In the training surgical cohort (n=1012): median age 59 yrs, 80% grade 2/3, 93% stage 1/2, 77% pN0, 23% pN1-3, ER+(87%)/PR+(81%)/HER2-(88%), 82 (8%) triple negative, 91 (9%) HER2+, with a 14% event rate and median 6-yrs of follow-up. PDxBRBx (Age + AI-grade) yielded a C-index of 0.72 (95% CI, 0.69-0.78) vs age C-index 0.61 (95% CI, 0.55 - 0.66) vs AI-grade 0.69 (95% CI, 0.64 - 0.73). Applying a risk score cut-off of 73 (scale 0-100) stratified patients as low- or high-risk, respectively, with a HR of 4.49 (95% CI 2.96 - 6.8, p<0.001) for predicting IBCFS, with Se 0.70, Sp 0.67, NPV 0.93, and PPV 0.25. In the biopsy validation cohort (n=776): median age 60 yrs, 43% Grade 2, ER+ (87%)/PR+(81%)/HER2-(88%), 56 (7%) triple negative, 97 (12.5%) HER2+ with a 14% event rate. PDxBRBx yielded a C-index of 0.73 (95% CI, 0.69-0.78) vs age 0.64 (95% CI, 0.57-0.70) vs AI-grade 0.69 (95% CI, 0.64-0.74). Patients stratified by a risk score of 73 had a HR of 4.49 (95% CI, 2.96-6.8, p<0.0001) for predicting IBCFS within 6 years, with Se 0.70, Sp 0.67, NPV 0.93, and PPV 0.25. The HR for AI-grade was 2.45 (95% CI 1-68-3.59, p<0.0001). All individual morphologic biopsy features including mitotic figure quantitation, nuclear pleomorphism, tumor-stromal ratio, lymphocytic content, and tumor architecture were statistically significant predictors of event risk (all p<0.05, most p<0.001). Comparison of biopsy (n=776) and matched excisional PreciseBreast risk scores showed substantial agreement, with Cohen’s κ = 0.57 (95% CI 0.51-0.62, p<0.0001) and an odds ratio of 31.4 (95% CI 18.8-55.1, p<0.0001). Conclusion We clinically validated a breast biopsy digital pathology-based AI prognostic test, PreciseBreast Biopsy, which successfully predicted IBCFS within 6 years. The test is designed to assist in the accurate characterization of clinical pathologic risk and patient management at the time of diagnosis. Additional studies in the neoadjuvant and possibly adjuvant settings will further refine the impact of these results on treatment selection. Citation Format: G. Fernandez, S. Vaisman, A. Sainath Madduri, R. Scott, M. Prastawa, X. Zhang, M. Donovan. Clinical validation of an Artificial Intelligence digital pathology-based prognostic test to predict risk of recurrence using biopsy specimens from patients with invasive breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-13-13.