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机器学习和后勤回归用于估计患有高恶性深层软组织肉瘤的患者的生存率:基于基于人口的回顾性队列的开发和分析

Andrea Thorn¹, Jessica A Lavery², Thomas Baad-Hansen³

¹Department of Orthopaedic Surgery, Rigshospitalet - University of Copenhagen, Copenhagen, Denmark. andrea.thorn@regionh.dk.

Acta orthopaedica

|March 10, 2026

概括

逻辑回归在预测软组织肉瘤患者5年生存期方面超过了机器学习模型. 干部位置,3级瘤和最近的化疗对生存结果产生了负面影响.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

软组织肉瘤 (STS) 是具有高转移潜力的侵袭性恶性瘤,主要发生在肺部.
准确的生存预测对于管理STS患者和长期治疗计划至关重要.
没有先前的机器学习 (ML) 存活模型利用现代斯堪的纳维亚人口为STS的数据.

研究的目的:

开发和比较ML模型与后勤回归用于5年STS生存预测.
确定影响STS患者生存的关键变量.
为了利用基于斯堪的纳维亚人口的数据集进行稳健的模型开发.

主要方法:

在丹麦 (2000-2016) 进行了对516名患者的回顾性队列研究,这些患者患有极端和干壁的深层,高等级的STS (2000-2016).
逻辑回归与四个ML模型进行比较,包括随机森林,使用70:30训练测试分割和5倍交叉验证.
通过曲线下的面积 (AUC),灵敏度,特异性和校准指标进行性能评估.

主要成果:

在试验组中,物流回归实现了0.74 (95% CI 0.66-0.82) 的优异AUC,而随机森林的AUC为0.65 (CI 0.56-0.74).
后勤回归显示了比随机森林更高的灵敏度 (0.65比0.59) 和特异性 (0.72比0.69).
随机森林显示Brier分数略低 (0.38对0.41),表明可比的校准性能.

结论:

在内部验证后,物流回归被证明比开发的随机森林ML模型更有效,用于在内部验证后5年STS生存预测.
瘤位置 (骨干),3级和最近的化疗 (<3个月) 被确定为显著的负预后因素.
需要进一步的外部验证,以评估这些预测模型对STS患者管理的临床实用性.

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