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放射学和机器学习用于脏瘤亚型评估,使用多相计算机断层扫描在多中心环境中

Annemarie Uhlig¹, Johannes Uhlig², Andreas Leha³

¹Department of Urology, University Medical Center Goettingen, Goettingen, Germany. Annemarie.uhlig@med.uni-goettingen.de.

European radiology

|April 18, 2024

概括

来自CT扫描的放射性特征的机器学习分析可以区分脏瘤亚型. 虽然在整体上是有效的,但细胞瘤呈现出了最具诊断挑战.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
人工智能的人工智能

背景情况:

准确的脏瘤组织学亚型确定对于治疗计划至关重要.
仅使用成像来区分脏瘤亚型可能具有挑战性.

研究的目的:

评估放射性特征和机器学习 (ML) 在区分瘤组织学亚型中的有效性.
根据多相计算机断层扫描 (CT) 数据评估ML模型的诊断性能.

主要方法:

对297名患有脏瘤的患者CT扫描的回顾性分析.
从动脉和静脉阶段CT扫描中提取放射性特征.
开发和验证一个极端梯度提升 (XGB) ML算法用于亚型分类.

主要成果:

在XGB模型中,接受器操作特征曲线 (AUC) 下的面积在静脉阶段为0.81,在训练队列中为组合阶段为0.8.
独立测试显示,静脉阶段的AUC为0.75,组合阶段的AUC为0.75.
血管肌脂瘤 (AML) 的鉴定准确度高 (AUC为0.9-0.94),而细胞瘤的鉴定准确度最低 (AUC为0.57-0.69).

结论:

使用ML的放射性特征分析可以在常规CT扫描上可靠地区分脏瘤亚型.
与静脉或组合相相比,动脉相 CT 放射性特征并没有显著改善亚型识别准确性.
细胞瘤是使用这种放射性方法区分最困难的亚型.

关键词:

计算机计算机计算机机器学习是机器学习.多检测器计算机断层扫描.无线电学 (Radiomics) 是一种无线电学.细胞癌是细胞癌.

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