Nottingham histologic grading is essential for breast cancer prognostication but suffers from inter-observer variability in assessing mitotic activity, nuclear pleomorphism, and tubule formation. We developed MOSAIC (Mammary Oncology Spatial Analysis and Intelligent Classification), an explainable AI framework designed to perform component-wise grading by independently modeling these three histologic features. Model outputs were calibrated using a two-phase pathology study to establish clinically reproducible scoring thresholds and were subsequently evaluated across public datasets and multi-institutional Indian cohorts. MOSAIC demonstrated robust performance, with AI-derived grades providing independent prognostic information (HR >= 1.8 in two datasets, p = < 0.001) and improved survival stratification compared to traditional methods. In pathologist calibration studies, AI-assisted scoring significantly reduced variability, specifically achieving near-perfect agreement in mitotic scoring with a weighted κ up to 0.98. Accuracy and Cohen’s kappa (κ) analysis further characterized the model’s technical performance across components: Tubule formation showed the highest agreement (Accuracy >= 0.6607, κ = 0.549), followed by overall Grade (Accuracy = 0.5637, κ = 0.539) and Mitotic activity (Accuracy = 0.4985, κ = 0.4), while Nuclear pleomorphism proved the most challenging (Accuracy = 0.3303, κ = 0.271). Comparative survival models confirmed that AI-derived grades were more significant predictors of risk than manual pathologist-assigned grades, with the AI model yielding a superior global p-value (5.9 × 10-7) and lower AIC (769.61). These results indicate that MOSAIC enables reproducible, interpretable grading by decomposing assessment into pathology-aligned components. By enhancing consistency while preserving prognostic relevance, this framework supports explainable AI as a viable assistive tool for routine breast cancer pathology.

The authors have declared no competing interest.

This study did not receive any funding

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Ethics committee of St John's Research Institute (SJRI), IEC/1/364/2023 gave ethical approval for this work. Prashanti Cancer Care Mission Independent Ethics Committee (2 November 2023), IEC project No. 0025102023, of Prashanti Cancer Care Mission (PCCM) gave ethical approval for this work. Sangini Ethics Committee (23 September 2024) of Samved Medicare Hospital gave ethical approval for this work. Pranayam Lung & Heart Institute Ethics Committee of Dr. Udayan's Laboratory (UL) gave ethical approval for this work. Ethics committee of Bharati Vidyapeeth Deemed to be University Medical College (BVDUMC), BVDUMC/IEC/215/25-26 gave ethical approval for this work. Zydus Hospital Ethics Committee (9 September 2022) of Zydus Hospitals gave ethical approval for this work.

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