Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study.

IF 15.1 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Lili Huang,Zhuoyuan Li,Xiaolei Zhu,Hui Zhao,Chenglu Mao,Zhihong Ke,Yuting Mo,Dan Yang,Yue Cheng,Ruomeng Qin,Zheqi Hu,Pengfei Shao,Ying Chen,Min Lou,Kelei He,Yun Xu
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引用次数: 0

Abstract

Early identification of cerebral small vessel disease related cognitive impairment (CSVD-CI) is crucial for timely clinical intervention. We developed a Transformer-based deep learning model using white matter hyperintensity (WMH) radiomics features from T2-fluid-attenuated inversion recovery images to detect CSVD-CI. A total of 783 subjects (161 longitudinally followed) were enrolled from three centres for model development and external validation, using a domain adaptation strategy. The model achieved AUCs of 0.841 (training) and 0.859/0.749 (validation cohorts), outperforming conventional machine learning models. The gradient-weighted class activation mapping approach highlighted WMH textural features, particularly the logarithm-transformed gray level size zone matrix features, as key contributors. These features were significantly correlated with CSVD macro- and microstructural changes, mediated age-cognition relationships and predicted longitudinal cognitive decline. Our findings indicate that WMH radiomics features, reflecting CI-related biological changes in CSVD, combined with a Transformer-based deep learning model, constitute a feasible, automated, and non-invasive tool for CSVD-CI detection.
深度自适应学习使用T2-FLAIR放射组学预测和诊断csvd相关的认知衰退:一项多中心研究。
早期识别脑血管病相关认知障碍(CSVD-CI)对于及时进行临床干预至关重要。我们开发了一个基于transformer的深度学习模型,利用t2流体衰减反演恢复图像中的白质高强度(WMH)放射组学特征来检测CSVD-CI。采用领域适应策略,共从三个中心招募了783名受试者(纵向随访161人)进行模型开发和外部验证。该模型的auc值为0.841(训练组)和0.859/0.749(验证组),优于传统的机器学习模型。梯度加权类激活映射方法突出了WMH纹理特征,特别是对数变换的灰度大小区域矩阵特征,是关键的贡献因素。这些特征与CSVD宏观和微观结构变化、介导的年龄认知关系和预测的纵向认知衰退显著相关。我们的研究结果表明,WMH放射组学特征反映了CSVD中ci相关的生物学变化,结合基于transformer的深度学习模型,构成了一种可行的、自动化的、无创的CSVD- ci检测工具。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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