Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Apr 16, 2022; 10(11): 3389-3400
Published online Apr 16, 2022. doi: 10.12998/wjcc.v10.i11.3389
Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients
Zi-Rui Ke, Wei Chen, Man-Xiu Li, Shun Wu, Li-Ting Jin, Tie-Jun Wang
Zi-Rui Ke, Wei Chen, Man-Xiu Li, Shun Wu, Li-Ting Jin, Tie-Jun Wang, Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
Author contributions: Ke ZR and Chen W originated the idea, data analysis and writing; Li MX, Wu S, Jin LT and Wang TJ contributed to the data analysis and writing; all authors have read and approved the manuscript.
Institutional review board statement: This study was approved by the Institutional Ethics Committee of the Hubei Cancer Hospital (Reference: LLHBCH2021YN-021), in compliance with the Declaration of Helsinki.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: None of the authors have any conflicts of interest to declare.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tie-Jun Wang, MD, Chief Doctor, Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, No. 116 Zhuodaoquan South Road, Hongshan District, Wuhan 430079, Hubei Province, China. tiejunwanghp@163.com
Received: October 25, 2021
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
Processing time: 164 Days and 21.3 Hours
Abstract
BACKGROUND

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.

AIM

To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Keywords: Breast cancer; Neoadjuvant chemotherapy; Clinical response; Machine learning; Prediction

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.