A reproducible open-data framework for interpretable breast cancer diagnosis and cytopathology education

Introduction Quantitative nuclear morphology links cytopathological assessment with interpretable diagnostic modeling and provides a practical foundation for teaching medical artificial intelligence. This study evaluated a reproducible framework that connects nuclear feature interpretation, malignant-versus-benign classification, and evidence-based assessment of diagnostic decisions. Methods The Wisconsin Diagnostic Breast Cancer dataset included 569 fine-needle aspiration samples (212 malignant and 357 benign) represented by 30 image-derived nuclear features. Seven supervised classifiers were compared using nested stratified cross-validation with five outer folds and four inner folds. Assessment covered ROC-AUC, precision-recall performance, probability calibration, training-derived decision thresholds, exploratory decision-curve analysis, feature importance, and leakage-controlled feature reduction. Results The radial basis function support vector machine achieved a ROC-AUC of 0.995, while the interpretable linear support vector machine (SVM) achieved 0.994, without a significant pairwise difference from the radial basis function SVM or logistic regression. For the linear SVM, fold-specific Youden thresholds yielded held-out accuracy of 0.981, sensitivity of 0.962, specificity of 0.992, F1-score of 0.974, and Matthews correlation coefficient of 0.959. Sensitivity-prioritized thresholding increased sensitivity to 0.991, with specificity of 0.866. The Brier score was 0.021. Nuclear texture, size, symmetry, and contour-related characteristics contributed prominently to model interpretation. A leakage-controlled 20-feature model retained a ROC-AUC of 0.996 and a precision-recall area under the curve of 0.994. Discussion The framework integrates quantitative cytopathology with reproducible model appraisal in a coherent teaching sequence. Nuclear feature distributions and importance patterns connect cellular morphology with diagnostic reasoning; classifier comparisons, calibration, and confusion matrices illustrate distinct dimensions of model performance; and alternative operating thresholds show how predictive probabilities translate into diagnostic decisions. Nested validation and reduced-feature analysis furnish practical examples for teaching data partitioning, feature redundancy, interpretability, and critical evaluation of medical AI. This shared analytical pathway provides case-based material for cytopathology, oncology, and interdisciplinary medical data science education.