Multimodal radiomics based on lesion connectome predicts stroke prognosis

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-03-01 DOI:10.1016/j.cmpb.2025.108701

Ning Wu , Wei Lu , Mingze Xu

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引用次数: 0

Abstract

Background

Stroke significantly contributes to global mortality and disability, emphasizing the critical need for effective prognostic evaluations. Connectome-based lesion-symptom mapping (CLSM) identifies structural and functional connectivity disruptions related to the lesion, while radiomics extracts high-dimensional quantitative data from multimodal medical images. Despite the potential of these methodologies, no study has yet integrated CLSM and multimodal radiomics for acute ischemic stroke (AIS).

Methods

This retrospective study analyzed lesion, structural disconnection (SDC), and functional disconnection (FDC) maps of 148 patients with AIS and assessed their association with the National Institutes of Health Stroke Scale (NIHSS) score at admission and prognostic outcomes, measured by the modified Rankin Scale at six months. Additionally, an innovative approach was proposed by utilizing the SDC map as mask, and radiomic features were extracted and selected from T1-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, susceptibility-weighted imaging, and fluid-attenuated inversion recovery images. Five machine learning classifiers were then used to predict the prognosis of AIS.

Results

This study constructed lesion, SDC and FDC maps to correlate with NIHSS scores and prognostic outcomes, thereby revealing the neuroanatomical mechanisms underlying neural damage and prognosis. Poor prognosis was associated with distal cortical dysfunction and fiber disconnection. Fifteen radiomic features within SDC maps from multimodal imaging were selected as inputs for machine learning models. Among the five classifiers tested, Categorical Boosting achieved the highest performance (AUC = 0.930, accuracy = 0.836).

Conclusion

A novel model integrating CLSM and multimodal radiomics was proposed to predict long-term prognosis in AIS, which would be a promising tool for early prognostic evaluation and therapeutic planning. Further investigation is needed to assess its robustness in clinical application.

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来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.