Auditing shortcut learning and misclassification in artificial intelligence-based breast cancer genomic subtyping

Abstract Objectives To simulate shortcut learning mechanisms in AI-based breast cancer genomic subtyping and to develop an interpretable, reproducible framework capable of auditing feature over-reliance using a low-code environment. Materials and Methods A retrospective secondary analysis of The Cancer Genome Atlas–Breast Invasive Carcinoma dataset was conducted. A multinomial logistic regression model was trained on 691 complete cases using only clinical predictors (estrogen receptor [ER], progesterone receptor [PR], HER2 status, tumor stage, and age) to predict PAM50 molecular subtypes, a genomic classification system originally developed by Parker et al, and widely used in breast cancer subtyping. A pseudo-Shapley additive explanations (SHAP) attribution method was implemented in Stata to estimate the marginal influence of each variable. Model performance was evaluated using McFadden’s pseudo-R2 and validated by comparing pseudo-SHAP scores against canonical SHAP values derived in Python. Statistical Framework of Pseudo-SHAP To enhance reproducibility, we now detail of the pseudo-SHAP methodology implemented in Stata. This approach estimates first-order marginal effects of each clinical predictor by simulating variable perturbations within a fitted multinomial logistic regression model. Specifically: 1. Predicted probabilities for each PAM50 subtype are generated using the baseline model. 2. Each predictor variable is perturbed individually (eg, PR status toggled from negative to positive), holding other features constant. 3. New probabilities are calculated postperturbation, and the change in probability (ΔP) for each class is recorded. 4. These ΔP values are averaged across all samples to provide directional importance scores, representing monotonic effects similar to mean SHAP values. This simulation assumes linear additive contributions and does not capture interaction or nonlinear effects. Results The clinical-only model achieved a pseudo-R2 of 0.396, indicating moderate explanatory power. Progesterone receptor and HER2 status were statistically significant predictors (P