Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database
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View abstract on PubMed
Summary
This summary is machine-generated.Breast-conserving surgery plus radiotherapy (BCS+RT) significantly improves long-term survival for early breast cancer patients receiving neoadjuvant systemic therapy (NST) compared to mastectomy. A machine learning model aids in personalized treatment decisions.
Area Of Science
- Oncology
- Surgical Oncology
- Biostatistics
Background
- Neoadjuvant systemic therapy (NST) is increasingly used for early breast cancer (EBC).
- Optimal surgical management (breast-conserving surgery plus radiotherapy [BCS+RT] vs. mastectomy) post-NST requires further clarification.
- Personalized treatment strategies are crucial for improving patient outcomes.
Purpose Of The Study
- To compare long-term outcomes of BCS+RT versus mastectomy in EBC patients after NST.
- To develop and validate a machine learning algorithm for personalized surgical treatment recommendations.
Main Methods
- Analysis of the Surveillance, Epidemiology, and End Results database (2010-2018) for EBC patients.
- Propensity score matching (PSM) to compare breast cancer-specific survival (BCSS) and overall survival (OS) between surgical groups.
- Development and validation of machine learning survival models, including random survival forest (RSF).
Main Results
- Among 13,958 patients, 64.7% received BCS+RT and 35.3% underwent mastectomy.
- After PSM, BCS+RT was associated with significantly improved BCSS (p<0.001) and OS (p<0.001) compared to mastectomy.
- The RSF model demonstrated superior predictive performance (training: 0.847, validation: 0.795) over other models and the COX model.
Conclusions
- BCS+RT offers improved long-term survival outcomes compared to mastectomy in EBC patients treated with NST.
- The validated RSF machine learning model can accurately predict patient outcomes.
- A web-based tool is available to guide clinical decision-making for surgical treatment selection.
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