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基于多种机器学习方法的放射学用于诊断CT图像上不可见的早期骨转移

Huili Wang¹, Jianfeng Qiu², Weizhao Lu²

¹College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, 250012, China.

Skeletal radiology

|July 19, 2024

概括

放射学和机器学习可以检测到在CT扫描上看不见的微观早期骨转移. K-最近邻居 (KNN) 分类器被证明是识别这些早期骨转移的最佳方法.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
在瘤学瘤学.

背景情况:

早期检测骨转移对于患者的治疗结果至关重要.
计算机断层扫描 (CT) 可能无法检测微观的早期骨转移.
对CT图像的放射学分析为增强检测提供了潜力.

研究的目的:

为了评估CT的放射学特征,与机器学习相结合,用于识别显微的早期骨转移.
为此任务确定最佳的机器学习方法.
使用[99mTc] - 甲基二酸盐 (MDP) 单光子发射计算断层扫描 (SPECT) 作为参考标准.

主要方法:

对63名早期骨转移患者的回顾性研究.
在CT图像上注册SPECT定义的感兴趣体积 (VOI).
从126个VOI中提取944个放射性特征 (63个转移,63个正常骨头).
开发和评估20个机器学习模型,使用5种特征选择和4种分类方法.

主要成果:

大多数模型都实现了接收器运行特征曲线 (AUC) 下的面积>0.70.
K-最近邻居 (KNN) 分类器显示出卓越的性能.
使用KNN分类器的XGBoost特征选择实现了最高的AUC:0.989 (训练) 和0.975 (测试).

结论:

放射学和机器学习可以识别CT无法看到的早期骨转移.
在这种应用中,KNN分类器是最佳的方法.

关键词:

这就是为什么CTCTCTCTCTCT 早期的骨转移会导致骨转移.机器学习机器学习无线电学 (Radiomics) 是一种辐射学.斯佩克特 (Spectre) 是一个运动场.

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