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在CT上自动化间歇性肺异常概率预测:波士顿肺癌研究中的逐步机器学习方法

Akinori Hata1, Kota Aoyagi1, Takuya Hino1

  • 1From the Center for Pulmonary Functional Imaging, Department of Radiology (A.H., T.H., N.W., V.I.V., M. Nishino, H.H.), and Pulmonary and Critical Care Division (G.M.H.), Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115; Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H., N.T.); Canon Medical Systems, Tochigi, Japan (K.A., Y.M., M. Nakatsugawa, A.K., N.S., M.O.); Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.H., N.W.); R&D Headquarters, Canon, Tokyo, Japan (M.K.); Department of Biostatistics, University of Michigan, Ann Arbor, Mich (J.S., Y.L.); Departments of Biostatistics (X.W., D.C.C.) and Environmental Health (D.C.C.), Harvard T.H. Chan School of Public Health, Boston, Mass; and Department of Imaging, Dana Farber Cancer Institute, Boston, Mass (M. Nishino).

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概括

自动化模型现在可以从CT扫描中预测间歇性肺异常 (ILAs) 的概率. 机器学习实现了高精度 (AUC 0.87),显示了用于识别ILAs的临床应用的潜力.

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 间歇性肺异常 (ILA) 具有临床意义,但CT扫描的自动检测尚未建立.
  • 准确识别ILA对于患者管理和进一步研究至关重要.

研究的目的:

  • 开发和验证机器学习模型,用于使用CT图像自动预测ILA概率.
  • 评估不同机器学习分类器在ILA检测中的性能.

主要方法:

  • 对来自波士顿肺癌研究的1382张CT扫描进行了回顾性分析.
  • 自动化的ILA概率预测模型是使用段式和案例推理模型的逐步方法构建的.
  • 机器学习分类器包括支持向量机 (SVM),随机森林 (RF) 和卷积神经网络 (CNN) 进行了评估,由专家放射学家确定了基本真相.

主要成果:

  • 性能最好的模型,使用三标签方法进行截面推理,使用两标签方法与RF进行案例推理,实现曲线下面面积 (AUC) 为0.87.
  • 在1382次扫描中,8%的确诊结果为ILA阳性,36%是不确定的,57%是负面的.
  • 该模型在估计ILA概率方面表现出了显著的表现.

结论:

  • 开发的自动化模型显示了临床应用的重大潜力,用于识别间歇性肺异常.
  • 这种人工智能驱动的方法可以帮助放射科医生在CT扫描上检测和评估ILA.