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基于ADHTransNet的放射学在多式垂体MRI上用于儿童的非侵入性激素预测

Qiang Zheng¹, Xiaolin Jiang¹, Jianzheng Sun²

¹School of Computer and Control Engineering, Yantai University, Yantai, China.

Computer methods and programs in biomedicine

|January 10, 2026

概括

这项研究引入了一个自动化的MRI管道来预测儿童的激素水平,减少了对侵入性刺激测试的需求. 非侵入性方法准确地预测生长激素和淋巴激素水平,有助于诊断与激素有关的疾病.

科学领域:

放射学放射学是指放射学
儿科内分泌学儿科内分泌学
人工智能在医学中的应用

背景情况:

生长激素缺乏症 (GHD) 和异常性中央早熟性青春期 (ICPP) 诊断通常涉及侵入性,耗时的刺激测试,包括多次抽血.
需要使用非侵入性,高效的方法来评估儿童的激素水平.

研究的目的:

开发一个完全自动化的管道,用于腺细胞细分和基于激素水平的放射学预测.
建立用于诊断GHD和ICPP的辅助工具,减少对侵入性测试的依赖.

主要方法:

使用了274名受试者 (548张MRI扫描:T1WI,T2WI) 包括GHD,ICPP和对照组.
开发了ADHTransNet用于自动化腺突细分和提取的放射学特征.
创建基于放射学的模型来预测生长激素 (GH) 和淋巴激素 (LH/FSH) 水平.

主要成果:

ADHTransNet在腺垂体细分方面表现优于其他深度学习方法.
放射学模型显示了与实际激素水平的显著相关性:高峰GH在GHD中 (ar-pGH的r=0.422,ins-pGH的r=0.359) 和ICPP中的LH/FSH (pLH的r=0.680,pLH的r=0.766).
在T1WI和T2WI图像上具有高测量和统计一致性的验证的放射学.

结论:

一个完全自动化的,多式,可复制和非侵入性的管道可以从MRI中预测GH和淋巴激素水平.
这种方法在减少在儿科内分泌评估中重复血液检测的需要方面显示出希望.
该管道增强了对儿童荷尔蒙相关疾病的评估.

关键词:

腺低化 (Adenohypophysis) 是一种发生在细胞内的过程.深度学习是一种深度学习.医疗图像细分医疗图像细分下垂体 - - 下垂体无线电学 (Radiomics) 是一种辐射学.

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