ArticleJournal of applied clinical medical physics2021
Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- CT texture phantom dataset with paired image quality assessments for quantitative imaging.Medical physics · 2026Article
- An open-source CT texture phantom for radiomic imaging studies: Design, manufacture, and initial evaluation.Medical physics · 2026Article
- Radiomics and Back Pain.Global spine journal · 2026Review
- Machine learning-based prediction of severe oral mucositis from head and neck cancer radiation therapy.Radiation oncology journal · 2025Article
- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.Journal of cancer research and clinical oncology · 2025Article
- Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning.Journal of imaging · 2025Article
- Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.Scientific reports · 2024Article
- Investigating the role of imaging factors in the variability of CT-based texture analysis metrics.Journal of applied clinical medical physics · 2024Article
- Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study.Dento maxillo facial radiology · 2023Article
- Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis.Scientific reports · 2023Article
- Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.European radiology · 2023Article
- Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis.Research square · 2023Article
- An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules.Quantitative imaging in medicine and surgery · 2023Article
- The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.Health data science · 2023Review
- Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions.Scientific reports · 2022Article
- CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.European radiology · 2022Article
- Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis.International journal of molecular sciences · 2022Article
- Mechanical and medical imaging properties of 3D-printed materials as tissue equivalent materials.Journal of applied clinical medical physics · 2022Article
- Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma.European journal of radiology open · 2022Article
Corrections and comments
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8 authors.
Funding
Abstract
objectiveThe objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND
methodsA custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps.
resultsA total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family.
conclusionThe choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
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