ArticleJournal of applied clinical medical physics2018
Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.
Article in Journal of applied clinical medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer.European radiology · 2025Pooled it
- Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.Contrast media & molecular imaging · 2020Pooled it
- Deep learning classification of significant prostate cancer on MRI: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- Multimodal CT radiomics predicts PD-1 inhibitor efficacy in advanced gastric cancer: a two-center validation study.Insights into imaging · 2025Article
- The value of using deep learning to predict proliferative hepatocellular carcinoma based on multiphasic magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Review
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Article
- Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.AJNR. American journal of neuroradiology · 2024Article
- Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization.Physics and imaging in radiation oncology · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.European radiology · 2024Article
- Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer.Acta radiologica (Stockholm, Sweden : 1987) · 2024Article
- Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of lung pathologies in neonates using dual-tree complex wavelet transform.Biomedical engineering online · 2023Article
- The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors.Cancers · 2023Article
- External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.European radiology · 2023Article
- Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren's Syndrome.Cancers · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.
methodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25-mm doped, gel-filled tubes, and 1 20-mm tube containing 0.25 mM Gd-DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight-channel head coil included T1WIs with multiple flip angles (flip-angle = 2,5,10,15,20,25,30), TR/TE = 4.09-5.47/0.90-1.35 ms, NEX = 1 and DCE with 30° flip-angle, TR/TE=4.09-5.47/0.90-1.35, and NEX = 1,4. DICOM data were imported into an in-house developed texture analysis program which extracted 41-texture features including histogram, gray-level co-occurrence matrix (GLCM), and gray-level run-length (GLRL). Two-tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip-angle, number of excitations (NEX), scanner platform).
resultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003-0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001-0.018) and scanner platform (Q < 0.0001), GLRL features (long-run emphasis, high gray-level run emphasis, high gray-level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003-0.022) and scanner platform (Q < 0.0001).
conclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.