BrCAI-Nexus: Translational Digital Pathology AI for Breast Cancer"”From Whole-Slide Biomarkers to Clinical Decision Support and Trial-Grade Evidence

Background: Breast cancer outcomes still rely on timely and accurate interpretation of tissue biopsies. Manual histopathological grading is labor"‘intensive, shows inter"‘ and intra"‘observer variability, and scales poorly for biomarker"‘driven clinical trials and companion diagnostics (CDx).

Objective: To describe BrCAI-Nexus (also known as DCS_PathIMS¹) "” a scientific framework implemented by the authors as an agentic AI digital pathology system for breast cancer whole-slide image (WSI) analysis"”currently delivering practitioner-approved, interpretable outputs for clinical decision support system (CDSS). This work also outlines planned extensions to: (1) generate quantitative digital biomarkers for patient stratification and recruitment in drug trials; (2) accelerate companion-diagnostic (CDx) co-development and Food and Drug Administration Premarket Approval (FDA-PMA) submissions through auditable AI and GenAI pipelines; and (3) enable PGx-integrated adaptive drug-target discovery.

Methods: Whole-slide images (WSIs) are curated through a governed preprocessing pipeline comprising scanner ingestion, image-level quality control, stain normalization, de-identification, and metadata harmonization. WSIs are tile-partitioned and analyzed using multi-task deep learning models for tumor segmentation, nuclei and mitosis detection, tubule formation scoring, pleomorphism assessment, tumor-infiltrating lymphocyte (TIL) quantification, and receptor-linked morphometric biomarkers (HER2, ER, PR, Ki-67). Slide-level and patient-level digital biomarkers are aggregated and mapped to CDSS decision pathways, CDx eligibility rules, trial-recruitment dashboards, and regulatory document templates. Multimodal fusion incorporates WSI phenotypes with molecular and PGx profiles to generate adaptive drug-target hypotheses.

Results: BrCAI-Nexus is expected to reduce grading variability, improve pathology turnaround times, decrease screen-failure rates in biomarker-stratified trials, and shorten clinical development cycles. Recent AI-pathology meta-analyses and trial case studies demonstrate diagnostic accuracy comparable to expert pathologists, improved reproducibility, and meaningful gains in operational efficiency across CDSS, CDx, and trial-support workflows.

Conclusion: Digitized biopsies analyzed with AI transform static histology into a longitudinal, quantitative map of cancer care. BrCAI-Nexus consolidates WSI-derived biomarkers, CDSS logic, CDx evidence generation, CRO trial acceleration, GenAI-enabled regulatory automation, and PGx-guided adaptive targeting"”supporting faster, safer, and more equitable precision oncology.