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细胞癌亚型分化的定量CT生物标志物:DECT,PCT和CT纹理分析的比较

Anjali Sah¹, Sneha Goswami¹, Amit Gupta¹

¹Department of Radio-diagnosis & Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India.

The British journal of radiology

|April 28, 2025

概括

双能量CT (DECT) 和CT纹理分析 (CTTA) 在区分细胞癌 (RCC) 亚型方面表现有前途. 在DECT上的比和CTTA上的是清细胞RCC (ccRCC) 的关键独立预测指标.

科学领域:

放射学和医学成像学医学成像学
在瘤学瘤学.
机器学习在医学中的应用

背景情况:

精确区分细胞癌 (RCC) 亚型,特别是清细胞RCC (ccRCC),对于预后和向治疗选择至关重要.
需要非侵入性成像生物标志物来区分ccRCC与非ccRCC,优化患者管理.
CT纹理分析 (CTTA), perfusion CT (PCT) 和双能CT (DECT) 是先进的成像技术,有可能对RCC进行表征.

研究的目的:

评估和比较CT纹理分析 (CTTA),输液CT (PCT) 和双能CT (DECT) 在区分清细胞RCC (ccRCC) 和非ccRCC的诊断性能.
在这些模式内确定ccRCC的独立成像预测因子.

主要方法:

追溯分析66名RCC患者 (52名ccRCC,14名非ccRCC) 接受了DECT和PCT.
对DECT (度,比) 和PCT (血液流量,血液体积,MTT,TTP) 参数进行定量分析.
从皮层甲状腺相图中提取CTTA特征;使用k-fold交叉验证开发用于分类区分的机器学习模型.

主要成果:

所有方法都显示出高的诊断准确性 (F1分数:PCT 0.9107,DECT 0.9358,CTTA 0.9348).所有方法都显示出高的诊断准确性 (F1分数:PCT 0.9107,DECT 0.9358,CTTA 0.9348).
在DECT上较高的比 (IR) (AUC 0.91) 和CTTA上增加的 (AUC 0.94) 是ccRCC的独立预测因素.
结合DECT,PCT和CTTA参数的机器学习模型实现了最高的诊断准确性 (F1分数为0.954).

结论:

PCT,DECT和CTTA在区分RCC亚型方面是有效的.
比 (DECT) 和 (CTTA) 是关键的独立成像标志物,对RCC的表征具有显著的临床实用性.
结合先进的CT技术为RCC亚型分化提供了卓越的诊断性能.

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