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Abstract <p>The cross-sectional area (CSA) of the cervical spinal cord (CSC) is a key biomarker for monitoring neurodegenerative diseases such as multiple sclerosis (MS). However, its clinical utility is limited by inter-subject variability (I-SV) due to anthropometric factors (height, sex) and MRI scanner differences, which can obscure true anatomical changes. This motivates the development of a normalization strategy to enhance the sensitivity of this established biomarker to pathological changes. Existing normalization methods rely on complex imaging features unavailable in many clinical datasets, thereby motivating the need for simpler approaches. We propose a lightweight regression-based normalization model for CSC-CSA using routinely available predictors. We evaluated the effect of these predictors across cervical spinal levels (CSL), demonstrating that the model captures a quantifiable proportion of I-SV reflected by a 15–23% reduction in the coefficient of variation (COV). Prior to normalization, CSC-CSA is measured using a novel fully automated landmark-based method. It is independent of vertebral labeling and spinal curvature, and uses spinal nerve midpoints as anatomical references. Model development was conducted using 267 healthy controls (HC) from the <i>Spine Generic</i> dataset. For validation, two external datasets were used: the public <i>OpenNeuro</i> dataset, comprising 10 HC, and a private dataset consisting of 23 scans acquired from 5 patients with MS. Bidirectional stepwise regression per CSL yielded adjusted <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> of 0.09–0.17. This approach offers a practical, generalizable framework for normalizing CSC-CSA, enabling accurate cross-sectional and longitudinal analyses in MS cohorts, as well as quantifying atrophy linked to progressive MS.</p> Graphical abstract <p></p>

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A lightweight method for regression-based normalization of the cross-sectional area for cervical spinal cord atrophy assessment

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Tayssir Boushila,
  • Mouna Sahnoun,
  • Fathi Kallel,
  • Salma Sakka,
  • Mariem Damak

摘要

Abstract

The cross-sectional area (CSA) of the cervical spinal cord (CSC) is a key biomarker for monitoring neurodegenerative diseases such as multiple sclerosis (MS). However, its clinical utility is limited by inter-subject variability (I-SV) due to anthropometric factors (height, sex) and MRI scanner differences, which can obscure true anatomical changes. This motivates the development of a normalization strategy to enhance the sensitivity of this established biomarker to pathological changes. Existing normalization methods rely on complex imaging features unavailable in many clinical datasets, thereby motivating the need for simpler approaches. We propose a lightweight regression-based normalization model for CSC-CSA using routinely available predictors. We evaluated the effect of these predictors across cervical spinal levels (CSL), demonstrating that the model captures a quantifiable proportion of I-SV reflected by a 15–23% reduction in the coefficient of variation (COV). Prior to normalization, CSC-CSA is measured using a novel fully automated landmark-based method. It is independent of vertebral labeling and spinal curvature, and uses spinal nerve midpoints as anatomical references. Model development was conducted using 267 healthy controls (HC) from the Spine Generic dataset. For validation, two external datasets were used: the public OpenNeuro dataset, comprising 10 HC, and a private dataset consisting of 23 scans acquired from 5 patients with MS. Bidirectional stepwise regression per CSL yielded adjusted \(R^2\) of 0.09–0.17. This approach offers a practical, generalizable framework for normalizing CSC-CSA, enabling accurate cross-sectional and longitudinal analyses in MS cohorts, as well as quantifying atrophy linked to progressive MS.

Graphical abstract