ArticleEJNMMI physics2021
Impact of segmentation and discretization on radiomic features in
Article in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.European radiology experimental · 2022Pooled it
- PET/CT contouring techniques in neuroendocrine neoplasms: a state-of-the-art scoping review.EJNMMI reports · 2026Review
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- Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors.Biomedicines · 2025Article
- Robustness of textural analysis features in quantitativeEJNMMI physics · 2025Article
- Inter- and intra-tumoral heterogeneity on [EJNMMI reports · 2024Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Retrospective evaluation of the predictive value of tumour burden at baseline [EJNMMI reports · 2024Article
- Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.Diagnostics (Basel, Switzerland) · 2024Review
- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs).Life (Basel, Switzerland) · 2023Review
- Prognostic Implications of PET-Derived Tumor Volume and Uptake in Patients with Neuroendocrine Tumors.Cancers · 2023Review
- Article
- Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors.Cancers · 2022Review
- Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer byFrontiers in oncology · 2021Article
- Radiomics-Based Texture Analysis ofFrontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from
methodsForty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUV
resultsDSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUV
conclusionsRFs robustness to manual segmentation resulted higher in NET
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