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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习算法的专家为中心的评估,用于大脑瘤细分.

Katharina V Hoebel1, Christopher P Bridge1, Sara Ahmed1

  • 1From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women's Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.).

Radiology. Artificial intelligence
|January 10, 2024
PubMed
概括

对脑瘤细分的深度学习显示出希望,但目前的评估指标与专家的临床感知不一致. 需要进一步的研究来改善癌症患者的质量评估.

关键词:
大脑瘤细分 脑瘤细分癌症 癌症 癌症 癌症深度学习算法 深度学习算法质母细胞瘤 (glioblastoma) 是一个机器学习 机器学习

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科学领域:

  • 医学成像分析 医学成像分析
  • 在瘤学中使用人工智能
  • 神经外科和放射科的神经外科和放射科

背景情况:

  • 深度学习算法越来越多地用于脑瘤细分.
  • 目前的评估实践往往依赖于定量指标,仅限于包括临床专家评估.
  • 评估细分质量对于有效的癌症治疗计划至关重要.

研究的目的:

  • 为了调查评估深度学习细分算法对脑瘤的现有实践.
  • 调查专家对细分质量的看法及其与定量指标的相关性.
  • 突出癌症研究中自动化指标和临床判断之间的差距.

主要方法:

  • 对180篇关于脑瘤细分算法的文献调查.
  • 收集了医疗专业人员对60例脑瘤细分病例的质量评级.
  • 分析了评价者之间的一致性和专家评价与标准指标 (迪斯,豪斯多夫距离) 之间的相关性.

主要成果:

  • 子得分,灵敏度和豪斯多夫距离是常见的指标,但专家评估很少 (2,8%的文章).
  • 在质量感知专家之间达成的低互评分协议 (Krippendorff α = 0.34).
  • 在专家评级和定量指标之间发现了弱相关性 (迪斯:0.23,豪斯多夫:0.51).

结论:

  • 专家对脑瘤细分的质量评级因模糊的界限和个体差异而变化很大.
  • 现有的定量指标不足以捕捉对细分质量的临床感知.
  • 开发更好地与癌症患者的临床相关性保持一致的评估方法至关重要.