基于能量谱CT指数的机器学习模型预测了下肢静脉血栓溶解的效果
Rong Liu1, Junlin Yang1, Hongkun Yin2
1Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Journal of applied clinical medical physics
|May 31, 2023
概括
这项研究开发了一种机器学习模型,使用计算机断层扫描的静脉扫描指数来预测下肢静脉血栓溶解的结果. 该模型和集成的诺摩图表显示出作为治疗规划的非侵入性术前工具的前景.
科学领域:
- 放射学和医学成像学 医学成像学
- 机器学习在医学中的应用
- 血管外科 血管外科
背景情况:
- 在下肢的静脉血栓塞栓症往往需要静脉内血栓溶解.
- 在手术前预测治疗成功对于患者管理至关重要.
- 目前的预测方法可能缺乏非侵入性精度.
研究的目的:
- 开发和验证一种非侵入性机器学习 (ML) 模型,使用能量频谱计算断层扫描 (CTV) 毒素学 (CTV) 指数.
- 在手术前预测下肢静脉血栓缓解治疗的有效性.
- 将ML模型得分与临床因素集成到名录中,以改善预测.
主要方法:
- 在18名患者的下肢静脉中分析了3492个切片 (好与差的血栓溶解预后).
- 使用单变量分析和皮尔森相关性选择关键的CTV指数.
- 开发一个支持向量机器分类器模型,具有十倍的交叉验证.
- 在每个切片和每个容器层面验证模型;构建一个名ogram.
主要成果:
- ML模型实现了高的曲线下面积 (AUC):0.838 (训练,每片),0.767 (验证,每片),0.945 (训练,每容器) 和0.876 (验证,每容器).
- 在验证数据集中,名图表表现出强的性能,AUC为0.901,精度为86.2%,灵敏度为87.9%,特异性为84.0%.
- 在预后良好和不良的组之间,没有发现血管分布的显著差异.
结论:
- 基于能量频谱CTV指数的ML模型有效地预测了血管层面的静脉血栓溶解结果.
- 开发的nomogram,整合临床因素和ML得分,提供了更好的预测性能.
- 这种方法具有作为指导下肢血栓溶解临床决策的非侵入性术前工具的潜力.
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