通过多观察者定性外观标记预测立体射线手术结果与MRI放射学相比
David A DeVries1,2, Terence Tang3, Ali Albweady4
1Department of Medical Biophysics, Western University, London, N6A 3K7, Canada. ddevrie8@uwo.ca.
Scientific reports
|November 28, 2023
概括
放射学机器学习模型用于大脑转移的立体射线外科手术结果显示,与定性分析相比,风险分层优越. 定性特征有助于解释这些先进的放射学模型.
科学领域:
- 放射学和成像科学 放射学和成像科学
- 瘤学和癌症研究研究
- 医疗人工智能 医疗人工智能
背景情况:
- 基于观察者的定性分析和基于T1w对比度增强的MRI的定量放射学分析都预测了大脑转移 (BM) 和立体辐射手术 (SRS) 的结果.
- 在这种情况下,基于放射学的机器学习 (ML) 模型的比较和解释有限.
研究的目的:
- 为了比较基于 BM-SRS 结果的定性和放射学分析的预测性能.
- 通过使用定性特征,研究基于放射学的ML模型的可解释性.
主要方法:
- 从99名患者的123个BM收集了数据,包括临床特征,T1w-CE MRI放射性特征和后SRS进展得分.
- 通过使用SRS剂量和定性BM外观评分来评估已发表的结果模型.
- 训练了基于放射学的ML模型来复制定性评分,并直接预测SRS后的进展.
主要成果:
- 在定性评分中观察者之间的高变异性对风险分层产生了负面影响.
- 复制定性评分的放射学模型获得了高准确度 (AUC 0.84-0.94),但风险分层较差.
- 与定性外观相比,直接预测进展的放射学模型显示风险分层增强 (p=0.0003).
结论:
- 对SRS的基于放射学的结果模型在风险分层方面优于定性外观分析.
- 定性外观为基于放射学的ML模型提供了关键的解释性,识别高风险的BMs (死核,异质).
- 将放射学与定性见解相结合,可以增强对BM-SRS结果和ML模型行为的理解.
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