NEURORADIOLOGY / LETTER TO THE EDITOR
Response to “On patient-level splitting, contrast-free claims and unsupported comparators in Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics”
1
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Submission date: 2026-02-18
Acceptance date: 2026-03-24
Publication date: 2026-05-25
Corresponding author
Mohammadreza Elhaie
Department of Medical Physics, School of Medicine Isfahan University of Medical Sciences
Department of Medical Physics, School of Medicine Isfahan University of Medical Sciences
Pol J Radiol, 2026; 91(1): 246-247
TOPICS
REFERENCES (6)
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Galassi SG. On patient-level splitting, contrast-free claims and unsupported comparators in “Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics”. Pol J Radiol 2026; 91: e56-e57. DOI: https://doi.org/10.5114/pjr/21....
2.
Elhaie M, Etemadifar M, Adariani AR, Khorasani A, Shahbazi-Gahrouei D. Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: a complete analysis of T1, T2, FLAIR, DWI, and SWI features. Pol J Radiol 2025; 90: e394-e403. DOI: 10.5114/pjr/206986.
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Roberts DR, Bahn V, Ciuti S, Boyce MS, Elith J, Guillera-Arroita G, et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 2017; 40: 913-929.
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Filippi M, Rocca MA, Ciccarelli O, De Stefano N, Evangelou N, Kap-pos L, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 2016; 15: 292-303.
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Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One 2015; 10: e0118432. DOI: 10.1371/journal.pone.0118432.
6.
Collins GS, Moons KG, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385: e078378. DOI: 10.1136/bmj-2023-078378.
| ISSN: | 1899-0967 |
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