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Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer

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Abstract

Purpose

The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

Methods

Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model.

Results

A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30–80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64–0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59–0.93), RD of 12.40% (95% CI: 8.01–16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16–15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28–16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93–11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type.

Conclusion

Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

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Data availability

This study analyzed public datasets that can be found here: the Surveillance, Epidemiology, and End Results Program (https://seer.cancer.gov/index.html).

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Funding

This work was supported by the Medical discipline Construction Health Committee of Project of Pudong Shanghai (Grant No.: PWYgV2021-02).

Author information

Author notes

  1. Enzhao Zhu and Linmei Zhang have contributed equally and share first authorship.

Authors and Affiliations

  1. School of Medicine, Tongji University, Shanghai, China

    Enzhao Zhu, Jiayi Wang, Huiqing Pan & Pu Ai

  2. Department of Periodontics, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, China

    Linmei Zhang

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

    Chunyu Hu

  4. Shanghai Hospital Development Center, Shanghai, China

    Weizhong Shi

  5. Columbia University, New York, NY, USA

    Ziqin Xu & Dan Shan

  6. National University of Ireland, Galway, Ireland

    Dan Shan

  7. Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China

    Zisheng Ai

  8. Clinical Research Center for Mental Disorders, Chinese-German Institute of Mental Health, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China

    Zisheng Ai

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  1. Enzhao Zhu
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Contributions

Critical revision of the manuscript for important intellectual content: EZ, DS, JW, CH, HP, and ZA. Statistical analysis: EZ and ZA. Obtained funding: DS and ZA. Administrative, technical, or material support: EZ, LZ, WS, ZX, PA, DS, and ZA. Supervision: DS and ZA.

Corresponding author

Correspondence to Zisheng Ai.

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The studies involving human participants were approved by the national cancer institution.

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Zhu, E., Zhang, L., Wang, J. et al. Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer. Breast Cancer Res Treat 205, 97–107 (2024). https://doi.org/10.1007/s10549-023-07237-y

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