ArticleEBioMedicine2020
Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.European journal of nuclear medicine and molecular imaging · 2022Trial
- Bridging radiomics and histopathology: cross-modality consistency of radiomic features from CT to micro-CT in NSCLC.European radiology experimental · 2026Article
- 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
- MRI-based habitat and peritumoral radiomics for predicting HER2 status in breast cancer: a multicenter study.Scientific reports · 2026Article
- Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.Breast (Edinburgh, Scotland) · 2026Review
- Investigation of clinical applicability of deep learning-based virtual contrast-enhanced MRI for nasopharyngeal carcinoma patients.Frontiers in oncology · 2026Article
- Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.NPJ precision oncology · 2025Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Review
- Development and application of a novel tumor habitat analysis technique based on dynamical modeling.Medical physics · 2025Article
- Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.Scientific reports · 2025Article
- Article
- Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.Tomography (Ann Arbor, Mich.) · 2025Article
- On the robustness of [18F]-FDG-PET radiomic features to variations in image acquisition and reconstruction settings: A phantom study.PloS one · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Review
- Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.Journal of applied clinical medical physics · 2024Article
7 more citing papers are in PubMed but not listed here.
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8 authors.
Funding
Abstract
backgroundRadiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial.
methodsTriple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images.
findingsSixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects.
interpretationTumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.
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