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深度学习与强大的联邦学习用于区分上腺转移与多相CT图像的良性病变

Bao Feng^1,2, Changyi Ma¹, Yu Liu²

¹Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China.

|February 19, 2024

概括

一个新的强大的联邦学习签名 (RFLS) 优于传统的深度学习,可以使用CT扫描来区分上腺转移和良性病变. 这种人工智能方法显著提高了诊断准确性,帮助临床决策.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

将上腺转移与腺瘤区分开来是一项挑战,特别是在以前患有恶性瘤的患者中.
准确的区分对于适当的患者管理和治疗规划至关重要.

研究的目的:

与传统的深度学习 (DLS) 相比,评估强大的联邦学习签名 (RFLS) 的性能,以区分上腺转移与良性上腺病变.
用三相计算机断层扫描 (CT) 成像来评估诊断的准确性.

主要方法:

对1187名患者进行三相CT扫描 (720例良性病变,467例转移) 的回顾性分析.
在CT图像上使用LASSO逻辑回归构建RFLS和DLS.
使用曲线下的面积 (AUC),净重新分类改进 (NRI) 和决策曲线分析 (DCA) 进行验证和比较.

主要成果:

在多个测试队列中,RFLS在区分转移与良性上腺病变方面表现出比DLS更好的诊断能力 (例如,平均AUC为0.816与0.798).
RFLS实现了更高的净重新分类改进 (NRI) 值,表明更好的分类准确性 (例如,NRI=0.643,P < 0.001).
决策曲线分析证实,与DLS相比,RFLS的临床效用和净益更大.

结论:

强大的联邦学习签名 (RFLS) 优于传统的深度学习签名 (DLS) 在手术前对上腺转移与良性上腺损伤的区分方面.
多相CT成像与RFLS相结合,为上腺损伤的特征提供了更好的诊断性能.

关键词:

上腺腺腺体是什么计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.联邦学习联邦学习转移转移转移转移

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