دورية أكاديمية
European Journal of Nuclear Medicine and Molecular Imaging / Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts
| العنوان: | European Journal of Nuclear Medicine and Molecular Imaging / Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts |
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| المؤلفون: | Papp, Laszlo, Grahovac, M., Spielvogel, C.P., Krajnc, D., Ecsedi, B., Traub‑Weidinger, T., Rasul, S., Kluge, K., Zhao, M., Li, X., Hacker, M., Haug, A. |
| بيانات النشر: | Springer |
| سنة النشر: | 2023 |
| المجموعة: | MedUni Vienna ePub (Medzinische Universität Wien) |
| مصطلحات موضوعية: | Machine learning, Radiomics, Fuzzy radiomics, PET, CT, MRI |
| جغرافية الموضوع: | UMW:14529, UMW:14636 |
| الوصف: | Background Hybrid 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. Methods Previously 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. Results Reference fuzzy (Ref-F) delineation outperformed its binary delineation (Ref-B) counterpart in all cohorts within a volume range of 938–354987 mm3 with relative cross-validation area under the receiver operator characteristics curve (AUC) of +4.7–10.4. Compared to Ref-B, the highest AUC performance difference was observed by the Ref-F delineation in the glioma cohort (Ref-F: 0.74 vs. Ref-B: 0.70) and in the prostate cohort by Ref-F and Ext-F (Ref-F: 0.84, Ext-F: 0.86 vs. Ref-B: 0.80). In addition, fuzzy radiomics decreased feature redundancy by approx. 20%. Conclusions Fuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text/html |
| اللغة: | English |
| تدمد: | 1619-7089 |
| العلاقة: | vignette : https://repositorium.meduniwien.ac.at/titlepage/urn/urn:nbn:at:at-ubmuw:3-70271/128Test; urn:nbn:at:at-ubmuw:3-70271; https://resolver.obvsg.at/urn:nbn:at:at-ubmuw:3-70271Test; local:99147385551903331; system:AC16849867 |
| DOI: | 10.1007/s00259-023-06127-1 |
| الإتاحة: | https://doi.org/10.1007/s00259-023-06127-1Test https://resolver.obvsg.at/urn:nbn:at:at-ubmuw:3-70271Test |
| حقوق: | cc-by_4 |
| رقم الانضمام: | edsbas.CC4EC535 |
| قاعدة البيانات: | BASE |
| تدمد: | 16197089 |
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| DOI: | 10.1007/s00259-023-06127-1 |