ArticleScientific reports2018
Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake.
Article in Scientific reports, 2018. 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.
- Current Position of Nuclear Medicine Imaging in Primary Bone Tumors.Diagnostics (Basel, Switzerland) · 2025Review
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma.Blood advances · 2023Article
- A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA.EJHaem · 2022Article
- Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake.PloS one · 2022Article
- Radiomics-Guided Precision Medicine Approaches for Colorectal Cancer.Frontiers in oncology · 2022Review
- Artificial Intelligence for Response Evaluation With PET/CT.Seminars in nuclear medicine · 2021Review
- Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening.Scientific reports · 2020Article
- Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging.Frontiers in oncology · 2020Article
- Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?European journal of nuclear medicine and molecular imaging · 2019Review
- Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.SM journal of clinical and medical imaging · 2018Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Identification of FDGavid- neoplasms may be obscured by high-uptake normal tissues, thus limiting inferences about the natural history of disease. We introduce a FDG-PET radiomics tissue classifier for differentiating FDGavid- normal tissues from tumor. Thirty-three scans from 15 patients with Hodgkin lymphoma and 68 scans from 23 patients with Ewing sarcoma treated on two prospective clinical trials were retrospectively analyzed. Disease volumes were manually segmented on FDG-PET and CT scans. Brain, heart, kidneys and bladder and tumor volumes were automatically segmented on PET images. Standard-uptake-value (SUV) derived shape and first order radiomics features were computed to build a random forest classifier. Manually segmented volumes were compared to automatically segmented tumor volumes. Classifier accuracy for normal tissues was 90%. Classifier performance was varied across normal tissue types (brain, left kidney and bladder, hear and right kidney were 100%, 96%, 97%, 83% and 87% respectively). Automatically segmented tumor volumes showed high concordance with the manually segmented tumor volumes (R
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