Computer methods and programs in biomedicine · 2026
AI and radiomics for improving the medical workflow for Multiple Sclerosis.
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Cipriani Alessia, Polsinelli Matteo, Placidi Giuseppe
Аннотация
BACKGROUND AND OBJECTIVE: Multiple Sclerosis (MS) is a chronic degenerative disorder that significantly affects the quality of life of patients. Magnetic Resonance Imaging (MRI) has become essential for diagnosis, monitoring, and treatment planning. However, variability in data acquisition and interpretation limits reproducibility. This study aims to design a standardized pipeline that integrates Artificial Intelligence (AI) and radiomics to improve precision, reproducibility, and clinical utility in the evaluation of MS.
METHODS: We propose a pipeline that incorporates preprocessing to harmonize imaging data, synthesis of missing modalities, automatic segmentation of lesions, and registration with anatomical and connectomic atlases. New radiomic features were extracted to quantify the characteristics of the lesions, their relationship to the nerve tracts, and the affected cortical regions.
RESULTS: The proposed pipeline stabilized resolution and contrast variability among different examinations, eliminated rater dependency in lesion segmentation, and enabled the extraction of new consistent radiomics. Indeed, the proposed radiomics captured the position and orientation of the lesions, as well as the involvement of nerves and cortical regions, offering additional information beyond the conventional lesion volume. Experiments demonstrated that the proposed radiomics are robust to inter-rater variability, being six times less sensitive than lesion volume, thus gaining reproducibility, specificity to lesion position/orientation, and precision.
CONCLUSIONS: This work introduces a structured and reproducible approach to integrate AI and radiomics into the clinical workflow for MS. By stabilizing imaging data and enabling advanced radiomic analyses, the pipeline supports predictive modeling. These findings suggest promising opportunities for future applications. However, the pipeline was validated on a limited public dataset; therefore, external clinical/radiological validation is required before considering its use in diagnosis or therapy-planning settings.
METHODS: We propose a pipeline that incorporates preprocessing to harmonize imaging data, synthesis of missing modalities, automatic segmentation of lesions, and registration with anatomical and connectomic atlases. New radiomic features were extracted to quantify the characteristics of the lesions, their relationship to the nerve tracts, and the affected cortical regions.
RESULTS: The proposed pipeline stabilized resolution and contrast variability among different examinations, eliminated rater dependency in lesion segmentation, and enabled the extraction of new consistent radiomics. Indeed, the proposed radiomics captured the position and orientation of the lesions, as well as the involvement of nerves and cortical regions, offering additional information beyond the conventional lesion volume. Experiments demonstrated that the proposed radiomics are robust to inter-rater variability, being six times less sensitive than lesion volume, thus gaining reproducibility, specificity to lesion position/orientation, and precision.
CONCLUSIONS: This work introduces a structured and reproducible approach to integrate AI and radiomics into the clinical workflow for MS. By stabilizing imaging data and enabling advanced radiomic analyses, the pipeline supports predictive modeling. These findings suggest promising opportunities for future applications. However, the pipeline was validated on a limited public dataset; therefore, external clinical/radiological validation is required before considering its use in diagnosis or therapy-planning settings.