ArticleJournal of digital imaging2018
Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
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- Radiomics in FAI: current status and perspectives.Bone & joint research · 2025Article
- Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.Scientific reports · 2025Article
- Radiomics to predict tumor response to combination chemoradiotherapy in squamous cell carcinoma of the anal canal: a preliminary investigation.European radiology experimental · 2025Article
- Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three-dimensional magnetic resonance imaging.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2024Article
- A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.Journal of imaging informatics in medicine · 2024Article
- Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- Article
- Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.Frontiers in endocrinology · 2024Article
- Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.Scientific reports · 2023Article
- Robustness of pulmonary nodule radiomic features on computed tomography as a function of varying radiation dose levels-a multi-dose in vivo patient study.European radiology · 2023Article
- MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures.Biomarker research · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features.BMC musculoskeletal disorders · 2023Article
- Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren's Syndrome.Cancers · 2023Article
- A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography.Frontiers in radiology · 2023Article
- A radiomics approach to the diagnosis of femoroacetabular impingement.Frontiers in radiology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC
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