Scholarlink

Objective <p>To develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the performance of clinicians in detecting SC lesions.</p> Materials and methods <p>A DL tool was developed based on SC sagittal T2 and STIR acquisitions from the imaging database of the French MS registry (OFSEP), including retrospective data from 40 different scanners. A multi-reader study based on retrospective data was performed between December 2023 and June 2024 to compare the performance of 20 clinicians in interpreting upper and lower SC acquisitions with and without the use of the tool. A ground truth was established by three experts. Sensitivity, precision, and inter-reader variability were evaluated.</p> Results <p>We included 50 patients (39 females, median age: 41 years [range: 15–67]) with SC MRI acquired between February 2017 and December 2022. When reading with the tool, the clinicians’ mean sensitivity to detect SC lesions improved (from 74.3% [95% CI = 67.8–80.6%] to 79.2% [95% CI: 73.5–85.0%]; <i>p</i> &lt; 0.0001), with no evidence of difference in the mean precision: (69.0% [95% CI: 62.8–75.2%] vs 70.1% [95% CI: 64.3–75.9%]; <i>p</i> = 0.08). Inter-reader variability in lesion detection was slightly improved with the tool (Light’s kappa = 0.55 vs 0.60), but without statistical difference (<i>p</i> = 0.056).</p> Conclusion <p>The use of an automatic tool can help clinicians detect SC lesions in pwMS by increasing their sensitivity.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>No tool to help detect MS SC lesions is used in clinical practice despite their frequency and prognostic value</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>This DL-based tool led to improvement in clinicians’ sensitivity in detecting SC lesions from both sagittal T2 and STIR sequences, without decreasing precision</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Our study indicated the potential of a DL-based tool to assist clinicians in the challenging task of detecting SC lesions in people with MS on a combination of sequences commonly acquired in clinical practice</i>.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月4日

查看全文
  • Baptiste Lodé,
  • Burhan Rashid Hussein,
  • Cédric Meurée,
  • Ricky Walsh,
  • Malo Gaubert,
  • Nicolas Lassalle,
  • Guilhem Courbon,
  • Agathe Martin,
  • Jeanne Le Bars,
  • Françoise Durand-Dubief,
  • Bertrand Bourre,
  • Adil Maarouf,
  • Olivier Outteryck,
  • Clément Mehier,
  • Alexandre Poulin,
  • Camille Cathelineau,
  • Jeremy Hong,
  • Guillaume Criton,
  • Sophie Motillon-Alonso,
  • Augustin Lecler,
  • Frédérique Charbonneau,
  • Loïc Duron,
  • Alexandre Bani-Sadr,
  • Céline Delpierre,
  • Jean-Christophe Ferré,
  • Gilles Edan,
  • François Cotton,
  • Romain Casey,
  • Francesca Galassi,
  • Benoit Combès,
  • Anne Kerbrat

摘要

Objective

To develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the performance of clinicians in detecting SC lesions.

Materials and methods

A DL tool was developed based on SC sagittal T2 and STIR acquisitions from the imaging database of the French MS registry (OFSEP), including retrospective data from 40 different scanners. A multi-reader study based on retrospective data was performed between December 2023 and June 2024 to compare the performance of 20 clinicians in interpreting upper and lower SC acquisitions with and without the use of the tool. A ground truth was established by three experts. Sensitivity, precision, and inter-reader variability were evaluated.

Results

We included 50 patients (39 females, median age: 41 years [range: 15–67]) with SC MRI acquired between February 2017 and December 2022. When reading with the tool, the clinicians’ mean sensitivity to detect SC lesions improved (from 74.3% [95% CI = 67.8–80.6%] to 79.2% [95% CI: 73.5–85.0%]; p < 0.0001), with no evidence of difference in the mean precision: (69.0% [95% CI: 62.8–75.2%] vs 70.1% [95% CI: 64.3–75.9%]; p = 0.08). Inter-reader variability in lesion detection was slightly improved with the tool (Light’s kappa = 0.55 vs 0.60), but without statistical difference (p = 0.056).

Conclusion

The use of an automatic tool can help clinicians detect SC lesions in pwMS by increasing their sensitivity.

Key Points

Question No tool to help detect MS SC lesions is used in clinical practice despite their frequency and prognostic value.

Findings This DL-based tool led to improvement in clinicians’ sensitivity in detecting SC lesions from both sagittal T2 and STIR sequences, without decreasing precision.

Clinical relevance Our study indicated the potential of a DL-based tool to assist clinicians in the challenging task of detecting SC lesions in people with MS on a combination of sequences commonly acquired in clinical practice.

Graphical Abstract