Abstract
Our study seeks to develop a predictive model using MRI-derived enlarged perivascular spaces (EPVSs) measurements and machine learning to assess cognitive impairment, subjective sleep quality, and excessive daytime sleepiness in young adults with long-time mobile phone use (LTMPU). We enrolled 82 participants and employed a pretrained deep learning model (VB-Net) to automatically segment EPVSs lesions across 17 brain subregions, extracting four handcrafted radiomic features – predefined based on morphological properties – per subregion (EPVSs count, volume, mean length, and mean curvature). The cohort was randomly divided into training (80%) and testing (20%) sets. Through minimum redundancy maximum relevance (mRMR) feature selection, six key biomarkers from 68 initial EPVSs metrics were identified combined with sex and age covariates. Final models were constructed using a Gaussian process (GP) classifier for cognitive impairment and decision tree (DT) algorithms for sleep quality and excessive sleepiness assessment. In testing, the GP model achieved an AUC of 0.818 (95% confidence interval [CI] 0.610-1) for cognitive impairment prediction. The DT models showed AUCs of 0.826 (95% CI: 0.616-1) for sleep quality and 0.875 (95% CI: 0.718-1) for daytime sleepiness. This automated radiomics pipeline demonstrates EPVSs morphological features as potential biomarkers for evaluating mobile phone exposure-related neurocognitive dysfunction. This automated radiomics pipeline suggests that EPVSs morphological features might be beneficial for evaluating mobile phone exposure-related neurocognitive dysfunction.
Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files. The data presented in this study are available on request from the corresponding author. Analysis utilized uAI research portal (v20240730) with open-source Python/scikit-learn/PyTorch implementations. De-identified data available upon request.
Code availability
The analytical workflow consists of two reproducible components: (1) EPVSs segmentation and feature quantification: Implemented via the uAI research portal (version 20240730, United Imaging Intelligence) – a commercially available standardized software platform. Any user with access to this software can obtain the same segmentation results and feature matrices (in tabular format) directly, without the need for custom coding or script manipulation. (2) Statistical analysis and predictive modeling: Custom code for this component is available on GitHub (https://github.com/simonsf/EPVS-Radiomics-ML). The code is built on open-source libraries: Python (v3.9.5), scikit-learn (v1.6.1), and PyTorch (v1.12.1). It includes complete implementations of z-score normalization, mRMR feature selection, GP/DT model training, model evaluation, and result visualization.
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Funding
This work was supported by the National Natural Science Foundation of China (grant number: 81801683, 82074303, 82174345, 81973684), Sichuan Key Research and Development Project (grant number: 2023YFS0226), Natural Science Foundation of Sichuan Province (grant number: 2023NSFSC1760), and Health Commission of Chengdu and Chengdu University of Traditional Chinese Medicine Joint Innovation Fund in 2024 (grant number: WXLH202403045, WXLH202403198, WXLH202402019, WXLH202403012).
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Li Li: Writing—review & editing, Writing—original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. Jiaojiao Wu: Writing—review & editing, Writing—original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. Bin Li: Writing—review & editing, Writing—original draft, Visualization, Formal analysis, Data curation. Rui Hua: Writing—review & editing, Writing—original draft, Visualization, Formal analysis, Data curation. Feng Shi: Writing—review & editing, Writing—original draft, Visualization, Formal analysis, Data curation. Lizhou Chen: Writing—review & editing, Supervision, Project administration, Methodology, Formal analysis. Yeke Wu: Writing—review & editing, Supervision, Project administration, Methodology, Formal analysis.
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Li, L., Wu, J., Li, B. et al. Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35845-3
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DOI: https://doi.org/10.1038/s41598-026-35845-3
