Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer.

Journal: NPJ precision oncology
Published Date: Jul 1, 2025

Abstract

This study developed a machine learning model for predicting the presence of tertiary lymphoid structures (TLSs) and treatment response to neoadjuvant therapy (NAT) in triple-negative breast cancer (TNBC). This multicenter study retrospectively included 697 patients, including the training cohort (n = 137), the TLS validation cohort (n = 63) and the NAT response validation cohorts (n = 560). Five machine learning models were developed to predict the presence of TLSs, and the XGBoost model, which exhibited the best performance, was selected as the radiomics-based TLS (rTLS) predictive model. The rTLS predictive model demonstrated robust predictive performance, including across various patient subgroups. Prognostic analysis showed that the rTLS predictive score was significantly correlated with disease-free survival (DFS) in TNBC receiving NAT, and was identified as a strong independent prognostic factor. Pathomic features further explained the pathological heterogeneity of TNBC with different responses to NAT. Overall, the rTLS predictive model, which accurately predicted the presence of TLSs and treatment response to NAT in TNBC, held promise for future clinical application in formulating personalized strategies for TNBC, ultimately improving prognosis, aiding in individualized patient treatment.

Authors

  • Yidan Lin
    Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian Province, China.
  • Yushuai Yu
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China; Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, 350014, China.
  • Qing Wang
    School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. qwang@163.com.
  • Kaiyan Huang
    School of Manufacturing Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, China.
  • Shukai Guo
    Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian Province, China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Yihui He
    Shandong First Medical University, Jinan, China.
  • Xin Yu
    eSep Inc., Keihanna Open Innovation Center @ Kyoto (KICK), Annex 320, 7-5-1, Seikadai, Seika-cho, Soraku-gun, Kyoto 619-0238, Japan.
  • Jiwen Zhang
    b Key Laboratory of Botanical Pesticide R&D in Shaanxi Province , Northwest A&F University , Yangling , People's Republic of China.
  • Fan Meng
  • Shicong Tang
    Department of Breast Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan Province, China. tang_shicong@126.com.
  • Junhui Yuan
    Department of Medical Imaging, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan Province, 450008, China. radiology2005@163.com.
  • Chuangui Song
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China; Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, 350014, China. Electronic address: songcg1971@outlook.com.

Keywords

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