| Autor: |
Jiahui Ren, Yili Li, Jing Zhou, Ting Yang, Jingfeng Jing, Qian Xiao, Zhongxu Duan, Ke Xiang, Yuchen Zhuang, Daxue Li, Han Gao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-72385-0 |
| Popis: |
Abstract This study aimed to compare the long-term outcomes of breast-conserving surgery plus radiotherapy (BCS + RT) and mastectomy in early breast cancer (EBC) patients who received neoadjuvant systemic therapy (NST), and sought to construct and authenticate a machine learning algorithm that could assist healthcare professionals in formulating personalized treatment strategies for this patient population. We analyzed data from the Surveillance, Epidemiology, and End Results database on EBC patients undergoing BCS + RT or mastectomy post-NST (2010–2018). Employing propensity score matching (PSM) to minimize potential biases, we compared breast cancer-specific survival (BCSS) and overall survival (OS) between the two surgical groups. Additionally, we trained and validated six machine learning survival models and developed a cloud-based recommendation system for surgical treatment based on the optimal model. Among the 13,958 patients, 9028 (64.7%) underwent BCS + RT and 4930 (35.3%) underwent mastectomy. After PSM, there were 3715 patients in each group. Compared to mastectomy, BCS + RT significantly improved BCSS (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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