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<p>The efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for Alzheimer’s disease (AD) is uncertain at baseline. Herein, we aimed to investigate whether radiomic features from the pre-treatment MRI data could predict rTMS efficacy for AD treatment. Out of 110 participants with AD in the active (<i>n</i> = 75) and sham (<i>n</i> = 35) rTMS treatment groups having T1-weighted brain MRI data, we had two groups of responders (active = 55 and sham = 24) and non-responders (active = 20 and sham = 11). We extracted histogram-based radiomic features from MRI data using 3D Slicer software; the most important features were selected utilizing a combination of a two-sample <i>t</i>-test, correlation test, least absolute shrinkage, and selection operator. The support vector machine classified rTMS responders and non-responders with a cross-validated mean accuracy/AUC of 81.9%/90.0% in the active group and 87.4%/95.8% in the sham group. Further, the radiomic features of the active group significantly correlated with participants’ AD assessment scale-cognitive subscale (ADAS-Cog) change after treatment (false discovery rate corrected <i>p</i> &lt; 0.05). Given that baseline radiomic features were able to accurately predict AD patients’ responses to rTMS treatment, these radiomic features warrant further investigation for personalizing AD therapeutic strategies.</p> Graphical Abstract <p></p>

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Using baseline MRI radiomic features to predict the efficacy of repetitive transcranial magnetic stimulation in Alzheimer’s patients

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Chandan Saha,
  • Chase R. Figley,
  • Brian Lithgow,
  • Xikui Wang,
  • Paul B. Fitzgerald,
  • Lisa Koski,
  • Behzad Mansouri,
  • Zahra Moussavi

摘要

The efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for Alzheimer’s disease (AD) is uncertain at baseline. Herein, we aimed to investigate whether radiomic features from the pre-treatment MRI data could predict rTMS efficacy for AD treatment. Out of 110 participants with AD in the active (n = 75) and sham (n = 35) rTMS treatment groups having T1-weighted brain MRI data, we had two groups of responders (active = 55 and sham = 24) and non-responders (active = 20 and sham = 11). We extracted histogram-based radiomic features from MRI data using 3D Slicer software; the most important features were selected utilizing a combination of a two-sample t-test, correlation test, least absolute shrinkage, and selection operator. The support vector machine classified rTMS responders and non-responders with a cross-validated mean accuracy/AUC of 81.9%/90.0% in the active group and 87.4%/95.8% in the sham group. Further, the radiomic features of the active group significantly correlated with participants’ AD assessment scale-cognitive subscale (ADAS-Cog) change after treatment (false discovery rate corrected p < 0.05). Given that baseline radiomic features were able to accurately predict AD patients’ responses to rTMS treatment, these radiomic features warrant further investigation for personalizing AD therapeutic strategies.

Graphical Abstract