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通过使用CT图像来描绘和预测癌症的分叶切除术后肺部的变化

Yanan Wu^1,2, Haowen Pang^1,2, Jing Shen^3,4

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Medical & biological engineering & computing

|August 24, 2023

概括

这项研究分析了肺部癌症的分管切除术后的肺部体积变化. 机器学习模型准确地预测了手术后的肺体积,有助于手术规划和患者预后.

科学领域:

胸部外科手术胸部外科手术
肺部医学肺部医学
医疗成像医学成像

背景情况:

腔切除术是局部肺癌的标准治疗方法.
了解空腹切除术后的肺体积变化对于患者的治疗结果至关重要.
准确预测术后肺体积可以优化手术规划.

研究的目的:

为了评估肺和叶片体积在叶片切除术后的变化.
使用机器学习预测术后肺体积.
评估叶切除术对肺生理和衰减的影响.

主要方法:

利用nnU-Net对135名患者进行自动肺叶细分.
测量了前后的肺和叶片体积,有效肺体积 (ELV) 和衰减.
开发了线性回归 (LR) 和多层感知子 (MLP) 模型来预测术后肺体积.

主要成果:

生理补偿导致肺总体体积 (TLV) 降低,在不同类型的肺切除术中从8.12%到13.46%不等.
叶片内减弱分布在叶片切除术后显示微小的变化.
LR和MLP模型的预测误差分别为9.8%和14.2%.

结论:

通过机器学习模型,可以有效地预测空腔切除术后的肺部体积.
放射性评估与预测模型相结合,可以改善叶切除术的规划.
改善手术规划可能会导致肺癌手术后患者的预后更好.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.肺部叶片切除术肺部叶片切除术音量变化音量变化

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