基于机器学习的放射学分析用于预测RAS突变状态,使用磁共振成像
Vincenza Granata1, Roberta Fusco2, Maria Chiara Brunese3
1Radiology Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy. v.granata@istitutotumori.na.it.
La Radiologia medica
|February 2, 2024
概括
规范化的MRI放射学特征可以准确预测肝转移中的RAS突变状态. 这种方法增强了手术前评估,提高了针对性治疗的诊断准确性.
科学领域:
- 放射学和医学成像学 医学成像学
- 在瘤学瘤学.
- 计算病理学计算病理学
背景情况:
- 在决定肝转移的治疗策略时,RAS突变至关重要.
- 准确的手术前预测RAS突变状态对于个性化的患者管理至关重要.
- 目前用于确定RAS状态的方法可能是侵入性的和耗时的.
研究的目的:
- 为了评估从磁共振成像 (MRI) 获得的放射学特征的有效性,使用肝脏特异性对比剂.
- 在手术前的环境中预测肝转移的RAS突变状态.
- 在这个预测任务中评估各种机器学习模型的性能.
主要方法:
- 在术前环境中对患者MRI扫描的回顾性分析.
- 使用3D Slicer和PyRadiomics提取851个放射性特征,遵守IBSI标准.
- 应用SASYNO用于样本平衡和统计分析,包括ROC,LRM和机器学习分类器 (DT,KNN,SVM).
主要成果:
- 来自动脉相的7个正常化的放射性特征,11个来自门相的特征,12个来自肝胆相和T2-W SPACE序列的特征被确定为强大的预测因素.
- 一个线性回归模型 (LRM) 结合了12个肝胆阶段的正常化特征,实现了99%的准确性,97%的灵敏性和100%的特异性.
- 与LRM相比,与测试的分类器 (DT,KNN,SVM) 没有观察到显著的准确性改善.
结论:
- 对MRI放射学分析的规范化方法对于预测RAS突变状态是有效的.
- 这种非侵入性方法有望改善肝转移的手术前评估.
- 需要进一步验证,以便将这种技术纳入临床实践.
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