计划基于CT的成像功能对于基于机器学习的疼痛反应预测的重要性
Óscar Llorián-Salvador1,2,3, Joachim Akhgar1, Steffi Pigorsch1
1Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.
Scientific reports
|October 13, 2023
概括
使用放射学和语义特征的机器学习模型显示,在疼痛的脊髓骨转移中,对放射治疗反应的预测有限. 临床特征为缓解疼痛提供了最好的预测准确度.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 疼痛的脊髓骨转移 (PSBMs) 是癌症护理中的一个常见挑战.
- 息性辐射疗法 (RT) 为大约三分之二的患者提供疼痛缓解.
- 预测治疗反应对于优化患者护理至关重要.
研究的目的:
- 评估机器学习 (ML) 模型,以预测PSBM患者对息性RT的完整疼痛反应.
- 为了比较放射学,语义和临床特征的预测性能.
- 评估ML在估计PSBMs治疗疗效方面的实用性.
主要方法:
- 从261名PSBM患者的CT扫描计划中提取了放射学,语义和临床特征.
- 使用了随机森林 (RFC) 和支持矢量机 (SVM) 分类器.
- 模型性能使用重复嵌套交叉验证进行了评估,采用接收器-操作器曲线下的面积 (AUROC) 作为主要指标.
主要成果:
- 放射学和语义ML模型实现了有限的预测性能 (AUROC ~0.62-0.63).
- 临床模型显示出卓越的预测准确性 (AUROC:0.80).
- 脊柱不稳定性新形成评分 (SINS) 显示中等预测能力 (AUROC:0.65),组合模型没有显著改善结果.
结论:
- 对CT扫描的放射学和语义分析为PSBM中的RT反应提供了有限的预测价值.
- 利用既定临床参数的ML模型为预测疼痛反应提供了最有前途的结果.
- 进一步的研究可能将重点放在整合多样化的临床数据,以提高息性瘤学的预测准确性.
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