Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.

Abstract

Purpose: This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additive Explanations (SHAP), feature importance, and coefficient effect size, we aim to provide insights into the significant factors influencing patient outcomes.

Methods: Data from 401 early-stage patients with breast cancer at the University of Missouri's Ellis Fischel Cancer Center were used, encompassing 20 variables related to demographics, tumor characteristics, and therapeutics. Six ML models, namely, Xtreme Gradient Boosting, Random Forest classifier, Logistic Regression, Decision Tree classifier (DT), Support Vector Machine classifier, and AdaBoost (ADB), were trained and evaluated using various performance metrics, including accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR). Feature importance, coefficient effect size, and SHAP values were used to interpret and visualize the importance of different features, particularly focusing on tumor quadrant variables.

Results: The extreme gradient boosting model outperformed other models, achieving an AUC-ROC score of 0.98 and an AUC-PR score of 0.97. The analysis revealed that tumor quadrant variables, especially the upper outer and miscellaneous or overlapping sites, were among the top predictive features for breast cancer survivability. SHAP analysis further highlighted the significance of these tumor locations in influencing survival outcomes.

Conclusion: This study demonstrates the efficacy of explainable ML models in predicting 5-year early-stage breast cancer survivability and identifies tumor quadrant location as an independent prognostic factor. The use of SHAP values provides a clear interpretation of the model's predictions, offering valuable insights for clinicians to refine treatment protocols and improve patient outcomes.