Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Abstract Background Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to a preoperative neoadjuvant approach in select cases, resulting in enhanced survival outcomes. A pathological complete response (pCR) is a critical prognostic marker, with higher pCR rates linked to improved overall and disease-free survival. Objective The objective of this study was to develop robust, machine learning–based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. Methods A retrospective cohort study was conducted using data from the Taipei Medical University Clinical Research Database from 2015 to 2022. Eligible patients were those with breast cancer who received neoadjuvant therapy followed by curative surgical resection. Machine learning models were developed using 3 distinct sets of variables. Model 1 included 14 clinical features such as age, height, weight, tumor stage, receptor status, tumor markers, and intrinsic subtype. Model 2 expanded on this by incorporating additional laboratory data and comorbidities (29 variables in total). Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. Feature selection was performed using recursive feature elimination with cross-validation, and model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUROC). Results A total of 334 patients were analyzed, with 199 in the non-pCR group and 135 in the pCR group. The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. Model 1 attained a mean accuracy of 0.66 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of laboratory data and comorbidities in model 2 did not yield significant enhancement, with a mean accuracy of 0.67 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of breast sonography response in model 3 led to a modest enhancement in predictive performance for the sonography group (accuracy 0.68; AUROC 0.60) in comparison to the nonsonography group (accuracy 0.66; AUROC 0.55). Despite the modest sample size (41 patients) of model 3, the integration of sonography data appeared to offer additional value in predicting pCR and warrants further investigation. Conclusions This study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. It is important to note that the findings of this study are preliminary and require cautious interpretation. Further studies are required to validate this approach and support its integration into a machine learning–based clinical workflow.