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Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.
Zhu, Enzhao; Zhang, Linmei; Liu, Yixian; Ji, Tianyu; Dai, Jianmeng; Tang, Ruichen; Wang, Jiayi; Hu, Chunyu; Chen, Kai; Yu, Qianyi; Lu, Qiuyi; Ai, Zisheng.
Afiliação
  • Zhu E; School of Medicine, Tongji University, Shanghai, China.
  • Zhang L; 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.
  • Liu Y; Department of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China.
  • Ji T; School of Medicine, Tongji University, Shanghai, China.
  • Dai J; School of Medicine, Tongji University, Shanghai, China.
  • Tang R; College of Electronic and Information Engineering, Tongji University, Shanghai, China.
  • Wang J; School of Medicine, Tongji University, Shanghai, China.
  • Hu C; Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
  • Chen K; College of Electronic and Information Engineering, Tongji University, Shanghai, China.
  • Yu Q; School of Medicine, Tongji University, Shanghai, China.
  • Lu Q; School of Medicine, Tongji University, Shanghai, China.
  • Ai Z; Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China. 2131237@tongji.edu.cn.
Clin Transl Oncol ; 26(10): 2584-2593, 2024 Oct.
Article em En | MEDLINE | ID: mdl-38678522
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Terapia Neoadjuvante / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Clin Transl Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Itália
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Terapia Neoadjuvante / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Clin Transl Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Itália