Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning

Enzhao Zhu; Linmei Zhang; Yixian Liu; Tianyu Ji; Jianmeng Dai; Ruichen Tang; Jiayi Wang; Chunyu Hu; Kai Chen; Qianyi Yu; Qiuyi Lu; Zisheng Ai

doi:10.1007/s12094-024-03459-8

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning

Clin Transl Oncol. 2024 Apr 28. doi: 10.1007/s12094-024-03459-8. Online ahead of print.

Authors

Enzhao Zhu^#¹, Linmei Zhang^#², Yixian Liu³, Tianyu Ji¹, Jianmeng Dai¹, Ruichen Tang⁴, Jiayi Wang¹, Chunyu Hu⁵, Kai Chen⁴, Qianyi Yu¹, Qiuyi Lu¹, Zisheng Ai^{6

7}

Affiliations

¹ School of Medicine, Tongji University, Shanghai, China.
² Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Prosthodontics, Stomatological Hospital and Dental School, Tongji University, Shanghai, 200072, China.
³ Department of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China.
⁴ College of Electronic and Information Engineering, Tongji University, Shanghai, China.
⁵ Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
⁶ Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China. 2131237@tongji.edu.cn.
⁷ Clinical Research Center for Mental Disorders, School of Medicine, Chinese-German Institute of Mental Health, Shanghai Pudong New Area Mental Health Center, Tongji University, Shanghai, China. 2131237@tongji.edu.cn.

^# Contributed equally.

PMID: 38678522
DOI: 10.1007/s12094-024-03459-8

Abstract

Background: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients.

Objective: To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL).

Methods: Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses.

Results: Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST.

Conclusions: Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.

Keywords: Breast cancer; Causal inference; Deep learning; Neoadjuvant systemic therapy.

Grants and funding

PWYgV2021-02/Medical discipline Construction Health Committee of Project of Pudong Shanghai