Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm

Abstract

Purpose

This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.

Methods

Data of 776 patients from a multicenter retrospective cohort study were collected. All patients had HER2-positive breast cancer and received NAC plus trastuzumab between 2001 and 2010. Two mathematical tools using a machine learning method were developed to predict the likelihood of disease-free survival (DFS) (DFS model) and brain metastasis (BM) (BM model) within 5 years after surgery. For validation, bootstrap analyses were conducted. The area under the receiver operating characteristics curve (AUC) was calculated to examine the discrimination.

Results

The AUC values were 0.785 (95% CI 0.740–0.831, P < 0.001) for the DFS model and 0.871 (95% CI 0.830–0.912, P < 0.001) for the BM model. Patients with low-risk DFS or BM events, as predicted by the models, showed better 5-year DFS and BM rates than those with high-risk DFS or BM events (89% vs. 61% for the DFS model, P < 0.001; 99% vs. 87% for the BM model, P < 0.001). These models maintained discrimination abilities in both luminal and non-luminal subtypes, providing prognostic information independent of pathological response. Bootstrap validation confirmed the high generalization abilities of the models.

Conclusions

The DFS and BM models have a high accuracy to predict prognosis among HER2-positive patients treated with NAC plus trastuzumab. Our models can help optimize adjuvant therapy and postoperative surveillance.

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