Observational studyScientific reports2019
Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial.
Observational study in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
76 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.Abdominal radiology (New York) · 2023Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Trial
- Early Whole-Body Diffusion-weighted MRI Helps Predict Long-term Outcome Following Peptide Receptor Radionuclide Therapy for Metastatic Neuroendocrine Tumors.Radiology. Imaging cancer · 2022Trial
- The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Article
- Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.European journal of radiology open · 2026Article
- A multimodal interpretable deep learning-radiomics framework for predicting lymph node metastasis following neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter validation study.NPJ precision oncology · 2026Article
- MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.Frontiers in oncology · 2026Article
- In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning.Scientific reports · 2025Article
- Radiomics for preoperative pancreatic ductal adenocarcinoma risk stratification: Cross-population validation, multidimensional integration, challenges, and future directions.World journal of radiology · 2025Article
- Article
- Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.Insights into imaging · 2025Article
- Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma.BJR artificial intelligence · 2025Article
- Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms.Frontiers in oncology · 2025Review
- T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study.BMC medical imaging · 2024Article
- Review
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study.BMC medical imaging · 2024Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Quantitative radiomics features, extracted from medical images, characterize tumour-phenotypes and have been shown to provide prognostic value in predicting clinical outcomes. Stability of radiomics features extracted from apparent diffusion coefficient (ADC)-maps is essential for reliable correlation with the underlying pathology and its clinical applications. Within a multicentre, multi-vendor trial we established a method to analyse radiomics features from ADC-maps of ovarian (n = 12), lung (n = 19), and colorectal liver metastasis (n = 30) cancer patients who underwent repeated (<7 days) diffusion-weighted imaging at 1.5 T and 3 T. From these ADC-maps, 1322 features describing tumour shape, texture and intensity were retrospectively extracted and stable features were selected using the concordance correlation coefficient (CCC > 0.85). Although some features were tissue- and/or respiratory motion-specific, 122 features were stable for all tumour-entities. A large proportion of features were stable across different vendors and field strengths. By extracting stable phenotypic features, fitting-dimensionality is reduced and reliable prognostic models can be created, paving the way for clinical implementation of ADC-based radiomics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.