Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database

• Published in: Scientific reports Vol. 14; no. 1; pp. 22055 - 12
• Main Authors: Ren, Jiahui, Li, Yili, Zhou, Jing, Yang, Ting, Jing, Jingfeng, Xiao, Qian, Duan, Zhongxu, Xiang, Ke, Zhuang, Yuchen, Li, Daxue, Gao, Han
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1059; 631/67/1347; 631/67/70; Adult; Aged; Breast cancer; Breast Neoplasms - mortality; Breast Neoplasms - surgery; Breast Neoplasms - therapy; Breast-conserving surgery; Early breast cancer; Epidemiology; Female; Humanities and Social Sciences; Humans; Learning algorithms; Long-term outcomes; Machine Learning; Mastectomy; Mastectomy, Segmental; Middle Aged; multidisciplinary; Neoadjuvant systemic therapy; Neoadjuvant Therapy - methods; Patients; Precision Medicine - methods; Prognosis; Radiation therapy; Science; Science (multidisciplinary); SEER Program; Survival; Mastectomy; Long-term outcomes; Early breast cancer; Breast-conserving surgery; Neoadjuvant systemic therapy; Machine learning

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 h...