ArticleEuropean journal of nuclear medicine and molecular imaging2022
Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The prognostic role of maximum tumor dissemination derived by PET/CT in oncological diseases: a systematic review.Frontiers in medicine · 2025Pooled it
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A Systematic Review.Journal of biomedical physics & engineering · 2026Article
- Multi-view deep learning for automated lymphoma staging fromEJNMMI research · 2026Article
- A proof of concept study ofScientific reports · 2025Article
- Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Pretreatment PET dissemination features as biomarkers for survival outcomes in melanoma patients treated with immune-checkpoint inhibitors.Scientific reports · 2025Article
- Value of PET radiomic features for diagnosis and reccurence prediction of newly diagnosed oral squamous cell carcinoma.Scientific reports · 2025Article
- Evaluating ΔMTV%, ΔDDiscover oncology · 2025Article
- Methodology Development for Investigating Pathophysiological [Biomedicines · 2025Article
- Radiomics based onQuantitative imaging in medicine and surgery · 2025Article
- Prognostic value of a simple distance index derived from PET maximum intensity projection.Frontiers in medicine · 2025Article
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Review
- A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Predicting T-Cell Lymphoma in Children FromJournal of imaging informatics in medicine · 2024Article
- Article
- Semiquantitative 2-[Frontiers in medicine · 2024Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBiomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [
methodsA total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined.
resultsModels with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs.
conclusionRegardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.
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