ArticleBreast cancer research and treatment2024
Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.
Article in Breast cancer research and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Review
- Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning.Cancer innovation · 2025Article
- Utilizing liquid-liquid biopolymer regulators to predict the prognosis and drug sensitivity of hepatocellular carcinoma.Biology direct · 2025Article
- Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound.Insights into imaging · 2024Article
- Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.Cancer innovation · 2024Article
- Personalized chemotherapy selection for patients with triple-negative breast cancer using deep learning.Frontiers in medicine · 2024Article
- Deep learning to optimize radiotherapy decisions for elderly patients with early-stage breast cancer: a novel approach for personalized treatment.American journal of cancer research · 2024Article
- Individualized survival prediction and surgery recommendation for patients with glioblastoma.Frontiers in medicine · 2024Article
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
purposeThe 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).
methodsSix 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.
resultsA 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.
conclusionOur results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.
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