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- Document type:
- Multicenter Study; Journal Article; Research Support, Non-U.S. Gov't
- Author(s):
- Bouman, Piet M; Noteboom, Samantha; Nobrega Santos, Fernando A; Beck, Erin S; Bliault, Gregory; Castellaro, Marco; Calabrese, Massimiliano; Chard, Declan T; Eichinger, Paul; Filippi, Massimo; Inglese, Matilde; Lapucci, Caterina; Marciniak, Andrzej; Moraal, Bastiaan; Morales Pinzon, Alfredo; Mühlau, Mark; Preziosa, Paolo; Reich, Daniel S; Rocca, Maria A; Schoonheim, Menno M; Twisk, Jos W R; Wiestler, Benedict; Jonkman, Laura E; Guttmann, Charles R G; Geurts, Jeroen J G; Steenwijk, Martijn D
- Title:
- Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection.
- Abstract:
- Background Cortical multiple sclerosis lesions are clinically relevant but inconspicuous at conventional clinical MRI. Double inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) are more sensitive but often unavailable. In the past 2 years, artificial intelligence (AI) was used to generate DIR and PSIR from standard clinical sequences (eg, T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery sequences), but multicenter validation is crucial for further implementation. Purpose To evaluate cortical and juxtacortical multiple sclerosis lesion detection for diagnostic and disease monitoring purposes on AI-generated DIR and PSIR images compared with MRI-acquired DIR and PSIR images in a multicenter setting. Materials and Methods Generative adversarial networks were used to generate AI-based DIR (n = 50) and PSIR (n = 43) images. The number of detected lesions between AI-generated images and MRI-acquired (reference) images was compared by randomized blinded scoring by seven readers (all with >10 years of experience in lesion assessment). Reliability was expressed as the intraclass correlation coefficient (ICC). Differences in lesion subtype were determined using Wilcoxon signed-rank tests. Results MRI scans of 202 patients with multiple sclerosis (mean age, 46 years ± 11 [SD]; 127 women) were retrospectively collected from seven centers (February 2020 to January 2021). In total, 1154 lesions were detected on AI-generated DIR images versus 855 on MRI-acquired DIR images (mean difference per reader, 35.0% ± 22.8; P < .001). On AI-generated PSIR images, 803 lesions were detected versus 814 on MRI-acquired PSIR images (98.9% ± 19.4; P = .87). Reliability was good for both DIR (ICC, 0.81) and PSIR (ICC, 0.75) across centers. Regionally, more juxtacortical lesions were detected on AI-generated DIR images than on MRI-acquired DIR images (495 [42.9%] vs 338 [39.5%]; P < .001). On AI-generated PSIR images, fewer juxtacortical lesions were detected than on MRI-acquired PSIR images (232 [28.9%] vs 282 [34.6%]; P = .02). Conclusion Artificial intelligence-generated double inversion-recovery and phase-sensitive inversion-recovery images performed well compared with their MRI-acquired counterparts and can be considered reliable in a multicenter setting, with good between-reader and between-center interpretative agreement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Zivadinov and Dwyer in this issue.
- Journal title abbreviation:
- Radiology
- Year:
- 2023
- Journal volume:
- 307
- Journal issue:
- 2
- Fulltext / DOI:
- doi:10.1148/radiol.221425
- Pubmed ID:
- http://view.ncbi.nlm.nih.gov/pubmed/36749211
- Print-ISSN:
- 0033-8419
- TUM Institution:
- Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler)
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