通过机器学习复制RECIST病变选择:对放射科医生内部和放射科医生之间的变异的洞察
Teresa M Tareco Bucho1,2, Liliana Petrychenko1,2, Mohamed A Abdelatty1,3
1Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
European journal of radiology open
|April 25, 2024
概括
机器学习模型复制了放射科医生的病变选择,以评估癌症治疗反应. 然而,放射科医生之间仍然存在显著的差异,这凸显了在应用RECIST标准方面需要更明确的指导方针.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医学成像分析 医学成像分析
背景情况:
- 固体瘤反应评估标准 (RECIST) 标准化了癌症治疗反应评估.
- 放射科医生之间病变选择的变化限制了RECIST的可复制性和准确性.
- 了解影响病变选择的因素对于改善RECIST应用至关重要.
研究的目的:
- 调查放射学家对可测量和目标病变选择的变异性贡献的因素.
- 评估机器学习模型复制放射科医生病变选择过程的能力.
主要方法:
- 机器学习模型从40名患者的胰腺癌数据集中训练了病变特征 (大小,形状,纹理,排名,近距离).
- 模型并行对两个放射科医生进行的病变选择.
- 除实验评估了病变直径,体积和排名的影响.
主要成果:
- 机器学习模型成功地重现了可测量的病变选择,主要是使用尺寸特征.
- 目标病变的选择被复制,主要是基于病变的排名.
- 即使在机器学习复制的情况下,仍然存在显著的放射科医生间变异性,这表明它依赖主观视觉特征.
结论:
- 虽然机器学习可以复制病变选择的各个方面,但放射科医生之间仍然存在重大分歧.
- 需要更精确的指导方针来标准化RECIST中的病变选择.
- 减少对个人解释的依赖对于弥合RECIST标准中的模糊性至关重要.
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