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开发基于SEER数据库的机器学习模型,用于早期乳腺癌患者接受新辅助全身疗法的个性化治疗策略

Jiahui Ren^1,2, Yili Li^1,2, Jing Zhou^1,2

¹Breast and Thyroid Surgery Department, Chongqing Health Center for Women and Children, Chongqing, China.

Scientific reports

|September 27, 2024

概括

与乳房切除相比,乳房保护性手术加放射治疗 (BCS+RT) 显著改善了接受新辅助系统治疗 (NST) 的早期乳腺癌患者的长期存活率. 一个机器学习模型有助于个性化治疗决策.

科学领域:

在瘤学瘤学.
手术瘤学手术瘤学
生物统计学生物统计学

背景情况:

新辅助全身疗法 (NST) 越来越多地用于早期乳腺癌 (EBC).
在NST后进行最佳的手术管理 (乳房保护手术加放射治疗[BCS+RT]与乳房切除术) 需要进一步澄清.
个性化治疗策略对于改善患者的治疗结果至关重要.

研究的目的:

为了比较经过NST后EBC患者的BCS+RT与乳腺切除术的长期结果.
开发和验证用于个性化手术治疗建议的机器学习算法.

主要方法:

对EBC患者的监测,流行病学和最终结果数据库 (2010-2018) 的分析.
倾向性得分匹配 (PSM) 用于在手术组之间比较乳腺癌特异性生存率 (BCSS) 和整体生存率 (OS).
机器学习生存模型的开发和验证,包括随机生存森林 (RSF).

主要成果:

在13958名患者中,64.7%接受BCS+RT,35.3%接受了乳腺切除术.
在PSM后,BCS+RT与乳房切除相比,与显著改善的BCSS (p<0.001) 和OS (p<0.001) 相关.
与其他模型和COX模型相比,RSF模型显示出更好的预测性能 (培训:0.847,验证:0.795).

结论:

与乳腺切除术相比,BCS+RT在接受NST治疗的EBC患者中提供了改善的长期生存结果.
经过验证的RSF机器学习模型可以准确预测患者的结果.
一个基于网络的工具可用于指导临床决策,用于手术治疗选择.

关键词:

乳腺保护手术是什么?乳腺癌的早期发展长期的结果.机器学习机器学习进行乳腺切除术.新辅助性全身治疗新辅助性全身治疗.

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