Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial
While conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. Prediction of symptom improvement one week after treatment cessation was poor, potentially due to the transient effects of stimulant treatment on symptom improvement.
Authors
- Anouk Schrantee (ORCID: https://orcid.org/0000-0002-4035-4845)
- Liesbeth Reneman (ORCID: https://orcid.org/0000-0002-5912-9971)
- Zarah van der Pal (ORCID: https://orcid.org/0009-0006-9895-4583)
- Henk A. Marquering (ORCID: https://orcid.org/0000-0002-1414-6313)
- Mingshi Chen (ORCID: https://orcid.org/0000-0003-1224-8399)
- Maarten G. Poirot (ORCID: https://orcid.org/0000-0003-1937-7978)
- Marco Bottelier (ORCID: https://orcid.org/0000-0003-1428-4123)
- Matthan W.A. Caan (ORCID: https://orcid.org/0000-0002-5162-8880)
- J. J. Sandra Kooij (ORCID: https://orcid.org/0000-0002-8644-6323)
Institutions
- University Medical Center Groningen (NL)
- Accare (NL)
- Amsterdam University Medical Centers (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- NeuroImage Clinical
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1016/j.nicl.2024.103707
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- ITEA
- China Scholarship Council