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在瘤学中提升预后:开发一种机器学习模型,用于预测未差异化形肉瘤患者的2年和5年生存率

Andrew G Girgis^1,2,3, Bishoy M Galoaa^1,2,4, Marcos R Gonzalez^1,2

¹Orthopaedic Oncology Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.

Annals of surgical oncology

|September 8, 2025

概括

一个新的机器学习 (ML) 算法准确地预测无差异性多形肉瘤 (UPS) 患者的2年和5年的整体存活率 (OS). 这种ML模型比软组织肉瘤的现有工具提供了更好的预后准确性.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

不差异化的多形肉瘤 (UPS) 是一种软组织肉瘤,预后不佳.
目前的UPS预测工具缺乏个性化数据和预测准确性.
机器学习 (ML) 为提高UPS患者的生存预测提供了机会.

研究的目的:

开发和验证一种新的ML算法,用于预测UPS患者的2年和5年的整体存活期 (OS).
为了提高无差异性多形肉瘤生存预测的准确性.

主要方法:

利用监测,流行病学和最终结果 (SEER) 数据库 (3494名患者) 进行培训和内部验证.
使用机构数据库 (288名患者) 进行外部验证.
开发了一个定制的神经网络,使用来自患者数据的高维嵌入来预测OS.

主要成果:

确定的关键生存预测因素包括瘤大小,年龄,转移,淋巴结参与和性别.
在内部验证中,ML模型获得了高性能 (AUC为2年OS0.81,5年OS0.82).
外部验证显示出优异的区分性表现 (AUC为2年OS0.79,5年OS0.81). 外部验证显示出优异的区分性表现 (AUC为2年OS0.79,5年OS0.81).

结论:

一个经过验证的ML算法准确地预测了UPS患者的2年和5年的OS.
开发的模型表现优于现有的预测方法.
建议进行进一步的外部验证,以确认可通用性.

关键词:

机器学习是机器学习.预后预测预测的预测.软组织肉瘤软组织肉瘤幸存的生存方式

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