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

• Enzhao Zhu ^[1] ; Tianyu Ji ^[1] ; Jianmeng Dai ^[1] ; Jiayi Wang ^[1] ; Qianyi Yu ^[1] ; Qiuyi Lu ^[1] ; Linmei Zhang ^[2] ; Yixian Liu ^[1] ; Ruichen Tang ^[1] ; Kai Chen ^[1] ; Chunyu Hu ^[3] ; Zisheng Ai ^[1]
  1. [1] Tongji University

     Tongji University

     China

     
  2. [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. [3] Tenth People’s Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China

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• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 26, Nº. 10, 2024, págs. 2584-2593
• Idioma: inglés
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• Resumen

  • 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 quantitativetreatment 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.