Predicting pathological response to neoadjuvant chemotherapy in breast cancer patients based on imbalanced clinical data

Neoadjuvant chemotherapy (NAC) may help some breast cancer patients with subsequent surgery or radiotherapy. However, there are certain risks associated with NAC. To lower the risks, machine-learning methods can be used to assist the diagnosis of breast tumors based on clinical data. This study investigated the use of ensemble machine-learning models in the prediction of pathological response to NAC for breast cancer patients with actual clinical data. The ensemble k-nearest neighbor (EKNN) model was determined to predict pathological responses. The imbalanced clinical data of patients with NAC were reviewed retrospectively, and 11 clinicopathological variables were selected from all features to establish succinct EKNN model. A total of 259 patients' clinical data was included in the model. The training and testing set for each single k-nearest neighbor (KNN) contained 27 and 9 patients, respectively. A total of 259 breast cancer patients in the database included 36 cases of pathological complete response, 157 cases of partial response, and 66 cases of stable disease. To solve the imbalanced clinical data problem, an ensemble-learning EKNN was designed, where the number of samples for each class in a base learner is set to equal to the minimum number 36. It showed that the classification accuracy of pathological response for breast cancer patients after NAC was 81.48% by EKNN model and the Kappa coefficient was 0.72, indicating that the robustness and generalization were better than the average prediction ability of single KNN model (average accuracy of single KNN model was 62.22% and Kappa coefficient was 0.43). Based on actual clinical data, important clinicopathological variables are selected, and the imbalanced problem are well solved by the ensemble EKNN model. The model improved the robustness and generalization for predicting the pathological response with imbalanced clinical data. It suggested that ensemble machine learning has possible practical applications for assisting cancer stage diagnoses and precision medicine.