ArticleEuropean journal of nuclear medicine and molecular imaging2023
Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- Regionally modulated radiomics analysis in PET/CT imaging: application to prognosis prediction of head and neck cancer.Physical and engineering sciences in medicine · 2026Article
- Fuzzy masks: boosting radiomic reliability in head and neck tumors amid delineation uncertainty.Physics and imaging in radiation oncology · 2026Article
- Revolutionizing HER-2 assessment: multidimensional radiomics in breast cancer diagnosis.BMC cancer · 2025Article
- Error mitigation enables PET radiomic cancer characterization on quantum computers.European journal of nuclear medicine and molecular imaging · 2023Article
- Article
- "Fuzzy" radiomics: the way forward for nuclear medicine imaging applications?European journal of nuclear medicine and molecular imaging · 2023Article
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Abstract
backgroundHybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to investigate the effect of recently proposed fuzzy radiomics and compare its predictive performance to conventional radiomics in cancer imaging cohorts. In addition, lesion vs. lesion+surrounding fuzzy and conventional radiomic analysis was conducted.
methodsPreviously published 11C Methionine (MET) positron emission tomography (PET) glioma, 18F-FDG PET/computed tomography (CT) lung, and 68GA-PSMA-11 PET/magneto-resonance imaging (MRI) prostate cancer retrospective cohorts were included in the analysis to predict their respective clinical endpoints. Four delineation methods including manually defined reference binary (Ref-B), its smoothed, fuzzified version (Ref-F), as well as extended binary (Ext-B) and its fuzzified version (Ext-F) were incorporated to extract imaging biomarker standardization initiative (IBSI)-conform radiomic features from each cohort. Machine learning for the four delineation approaches was performed utilizing a Monte Carlo cross-validation scheme to estimate the predictive performance of the four delineation methods.
resultsReference fuzzy (Ref-F) delineation outperformed its binary delineation (Ref-B) counterpart in all cohorts within a volume range of 938-354987 mm
conclusionsFuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared to conventional binary radiomics in PET. We hypothesize that this effect is due to the ability of fuzzy radiomics to model partial volume effects and delineation uncertainties at small lesion boundaries. In addition, we consider that the lower redundancy of fuzzy radiomic features supports the identification of imaging biomarkers in future studies. Future studies shall consider systematically analyzing lesions and their surroundings with fuzzy and binary radiomics.
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