{"title":"Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study.","authors":"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","doi":"10.1038/s41746-025-01813-w","DOIUrl":null,"url":null,"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.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"2 1","pages":"444"},"PeriodicalIF":15.1000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Digital Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41746-025-01813-w","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
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.
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.