ArticleEuropean radiology2022
Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Can ADC differentiate cellular from acellular mucin in mucinous adenocarcinoma tumor beds after treatment of rectal cancer? A multicenter study.European radiology · 2026Article
- Predicting locoregional treatment failure in anal cancer using multivariable clinical-imaging models.Clinical and translational radiation oncology · 2026Article
- Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.BMC cancer · 2026Article
- Challenges in rectal cancer MRI: from image acquisition to interpretation.Abdominal radiology (New York) · 2026Review
- A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.Insights into imaging · 2026Article
- Reproducibility of MRI Radiomics Measurements in Men with Prostate Cancer Undergoing Active Surveillance.Cancers · 2026Article
- Improving rectal tumor segmentation with anomaly fusion derived from anatomical inpainting: a multicenter study.Scientific reports · 2026Article
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- Cross species reproducibility of MRI radiomics features enables intervertebral disc degeneration assessment in experimental monkeys.Scientific reports · 2025Article
- Reliability of rectal MRI radiomic features: Comparing rectal MRI radiomic features across reader expertise levels, image segmentation technique, and timing of rectal MRI in patients with locally advanced rectal cancer.European journal of radiology · 2025Article
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping.The international journal of cardiovascular imaging · 2025Article
- Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features.Scientific reports · 2024Article
- Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA.Neuroinformatics · 2024Article
- Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2023Article
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Pearls and pitfalls of structured staging and reporting of rectal cancer on MRI: an international multireader study.The British journal of radiology · 2023Article
Corrections and comments
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Authors and funding
18 authors at 13 institutions in 3 countries.
Funding
Abstract
objectivesTo investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software.
methodsT2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient.
resultsImage features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41).
conclusionsSignificant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. KEY POINTS: • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.
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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.