Research ArticleOriginal Article

A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity

Yusong Wang, Mozhi Wang, Keda Yu, Shouping Xu, Pengfei Qiu, Zhidong Lyu, Mingke Cui, Qiang Zhang and Yingying Xu
Cancer Biology & Medicine March 2023, 20 (3) 218-228; DOI: https://doi.org/10.20892/j.issn.2095-3941.2022.0513

Article Information

vol. 20 no. 3 218-228
DOI 
https://doi.org/10.20892/j.issn.2095-3941.2022.0513
Published By 
Cancer Biology & Medicine
History 
  • Received August 22, 2022
  • Accepted January 16, 2023
  • Published online March 24, 2023.
Copyright & Usage 
Copyright: © 2023, Cancer Biology & Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Author Information

  1. Yusong Wang1,*,
  2. Mozhi Wang1,*,
  3. Keda Yu2,
  4. Shouping Xu3,
  5. Pengfei Qiu4,
  6. Zhidong Lyu5,
  7. Mingke Cui6,
  8. Qiang Zhang6⇑ and
  9. Yingying Xu1⇑
  1. 1Department of Breast Surgery, The First Hospital of China Medical University, Shenyang 110001, China
  2. 2Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
  3. 3Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, China
  4. 4Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan 250117, China
  5. 5Breast Center, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
  6. 6Department of Breast Surgery, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
  1. Correspondence to: Qiang Zhang and Yingying Xu, E-mail: zhangqiang8220{at}163.com and xuyingying{at}cmu.edu.cn
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Funding

  • National Natural Science Foundation of China

    82203786

  • Natural Science Foundation of Liaoning Province of China

    2022-YGJC-68

  • Chinese Young Breast Experts Research project

    CYBER-2021-A02

This work was supported by the National Natural Science Foundation of China (Grant No. 82203786), the Natural Science Foundation of Liaoning Province of China (Grant No. 2022-YGJC-68), and Chinese Young Breast Experts Research project (Grant No. CYBER-2021-A02).

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