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Background <p>Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-established involvement of cortical lesions in MS, feasibility limitations in their visualization on typical magnetic resonance imaging (MRI) protocols prevent their evaluation in nearly all clinical trials. Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data. These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.</p> Methods <p>We sought to evaluate the feasibility of combining and extending existing methods into a unified framework for analysis using the data from the large, multicenter, phase 3 ORATORIO trial (full <i>n</i> = 732, age=44.6 ± 8.0; development subset <i>n</i> = 80, age=46.6 ± 7.1). We specifically evaluated three of the most promising of them – fluid-attenuated inversion recovery squared (FLAIR<sup>2</sup>), T1/T2 ratio, and artificial intelligence-derived double inversion recovery (AI-DIR) – and introduced a new combined contrast called multi-modal cortical lesion enhanced (MMCLE). We also harnessed transformer-based semantic segmentation to improve automated detection and delineation of these lesions.</p> Results <p>At baseline, we detected 14.8 + /−20.72 lesions per participant, with 86.0% true positive rate and 8.4% false positive rate across subjects for blinded MMCLE, using simultaneous review of all contrasts as the reference. High reproducibility was observed across field strengths and acquisition types (ICC 88.8-92.5%).</p> Conclusions <p>We confirmed that cortical lesions can be clearly visualized and quantified with these methods. Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.</p>

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Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Michael G. Dwyer,
  • Niels Bergsland,
  • Alexander Bartnik,
  • Dejan Jakimovski,
  • Samantha Noteboom,
  • Menno M. Schoonheim,
  • Martijn D. Steenwijk,
  • Jinglan Pei,
  • David Clayton,
  • Robert Zivadinov

摘要

Background

Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-established involvement of cortical lesions in MS, feasibility limitations in their visualization on typical magnetic resonance imaging (MRI) protocols prevent their evaluation in nearly all clinical trials. Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data. These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.

Methods

We sought to evaluate the feasibility of combining and extending existing methods into a unified framework for analysis using the data from the large, multicenter, phase 3 ORATORIO trial (full n = 732, age=44.6 ± 8.0; development subset n = 80, age=46.6 ± 7.1). We specifically evaluated three of the most promising of them – fluid-attenuated inversion recovery squared (FLAIR2), T1/T2 ratio, and artificial intelligence-derived double inversion recovery (AI-DIR) – and introduced a new combined contrast called multi-modal cortical lesion enhanced (MMCLE). We also harnessed transformer-based semantic segmentation to improve automated detection and delineation of these lesions.

Results

At baseline, we detected 14.8 + /−20.72 lesions per participant, with 86.0% true positive rate and 8.4% false positive rate across subjects for blinded MMCLE, using simultaneous review of all contrasts as the reference. High reproducibility was observed across field strengths and acquisition types (ICC 88.8-92.5%).

Conclusions

We confirmed that cortical lesions can be clearly visualized and quantified with these methods. Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.