Published online Apr 16, 2022. doi: 10.12998/wjcc.v10.i11.3389
Peer-review started: October 25, 2021
First decision: December 17, 2021
Revised: December 23, 2021
Accepted: February 27, 2022
Article in press: February 27, 2022
Published online: April 16, 2022
Complete response after neoadjuvant chemotherapy (rNACT) elevates the surgical outcomes of patients with breast cancer, however, non-rNACT have a higher risk of death and recurrence.
To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT.
A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for rNACT by multiple ML-based algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance.
Analysis identified several significant differences between the rNACT and non-rNACT groups, including total cholesterol, low-density lipoprotein, neutrophil-to-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine (SVM) model with twelve variables introduced was identified as the best predictive model.
By incorporating pretreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of rNACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of rNACT in patients with breast cancer.
Core Tip: For predicting response after neoadjuvant chemotherapy (rNACT), some machine learning-based models performed better than models using conventional methods, and the support vector machine model performed best. Preoperative serum lipids and serum inflammation markers have contributed to predicting rNACT in breast cancer patients. These results suggested the need to raise awareness of the importance of minimally-invasive approaches for monitoring breast cancer patients who intended to undergo neoadjuvant chemotherapy. However, the current study needs to be validated with caution and require external validation in the future.