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人工智能作为风险主导肺结节的独立读者:CT重建参数的影响

Yifei Mao^1,2, Marjolein A Heuvelmans^1,2,3, Marcel van Tuinen⁴

¹Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

European radiology

|August 29, 2025

概括

重建核的设置在低剂量CT肺癌查中显著影响AI结节的分类,但不影响检测. 将人工智能内核与放射科医生的偏好对齐可以提高性能和临床整合.

科学领域:

医学成像
放射学中的人工智能
肺癌查

背景情况:

肺癌查依赖于准确识别和分类风险主导的结节.
人工智能 (AI) 在分析低剂量CT扫描 (LDCT) 肺结节方面表现有前途.
对于风险主导结节的AI性能,CT重建参数的影响尚不清楚.

研究的目的:

评估不同重建参数如何影响AI在基线LDCT查中检测和分类风险主导结节的能力.
在各种内核和厚度/间隔设置中比较AI性能.

主要方法:

人工智能使用四个重建设置分析了300名参与者的LDCT扫描.
通过两位放射科医生阅读的共识来评估性能.
这些指标包括扫描级别的灵敏度,特异性,结节检测率和结节类型分类协议.

主要成果:

与AI-B80f/ 1mm相比,AI-D45f/ 1mm对风险主导结节的敏感性显著提高 (77. 5%对31. 5%).
核的变化对结节类型分类协议有显著影响 (87.7%对17.7%),但检测率没有 (87.4%对82.0%).
厚度/间隔设置的变化没有显著影响AI性能.

结论:

重建内核的选择对LDCT肺癌查中的AI结节分类准确性产生了重大影响.
块检测率对核变异的敏感度较低.
将人工智能重建内核与放射科医生的偏好对齐可以提高人工智能性能并促进临床整合.

关键词:

人工智能进行分类计算机辅助诊断一个人的肺结节电脑断层扫描

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