ArticleZeitschrift fur medizinische Physik2023
Feasibility of radiomic feature harmonization for pooling of [
Article in Zeitschrift fur medizinische Physik, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.Medical physics · 2025Article
- An evaluation of image-based and statistical techniques for harmonizing brain volume measurements.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [European journal of nuclear medicine and molecular imaging · 2024Article
- Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.Scientific reports · 2024Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Review
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- Clinical Applications of TSPO PET for Glioma Imaging: Current Evidence and Future Perspective-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Multi-site harmonization for magnetoencephalography spectral power data.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLarge datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated.
methods[
resultsAccording to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and inter-rater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [
conclusionsAccording to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmonization. Thus, for clinical utilization it is recommended to exclude affected features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.