Observational studyMedicine2020
Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.
Observational study in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CT image-derived radiomics predicts molecular subtypes in bladder urothelial carcinoma: validation of a non-invasive classification strategy.Scientific reports · 2026Article
- Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.Abdominal radiology (New York) · 2024Article
- Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer.Discover oncology · 2024Article
- Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.Heliyon · 2024Article
- Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles?Cureus · 2023Article
- Computed Tomography Urography: State of the Art and Beyond.Tomography (Ann Arbor, Mich.) · 2023Review
- Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study.The British journal of radiology · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of histologic grading of urothelial carcinoma still depends on histopathologic examination. As an emerging and promising imaging technology, radiomic texture analysis is a noninvasive technique and has been studied to differentiate various tumors. This study explored the value of computed tomography (CT) texture analysis for the differentiation of low-grade urothelial carcinoma (LGUC), high-grade urothelial carcinoma (HGUC), and their invasive properties.Radiologic data were analyzed retrospectively for 94 patients with pathologically proven urothelial carcinomas from November 2016 to April 2019. Pathologic examination demonstrated that tumors were: high grade in 43 cases, and low grade in 51 cases; and nonmuscle invasive (NMI) in 37 cases, and muscle invasive (MI) in 37 cases. Maximum tumor diameters on CT scan were manually outlined as regions of interest and 78 texture features were extracted automatically. Three-phasic CT images were used to measure texture parameters, which were compared with postoperative pathologic grading and invasive results. The independent sample t test or Mann-Whitney U test was used to compare differences in parameters. Receiver-operating characteristic curves for statistically significant parameters were used to confirm efficacy.Of the 78 features extracted from each phase of CT images, 26 (33%), 20 (26%), and 22 (28%) texture parameters were significant (P < .05) for differentiating LGUC from HGUC, while 19 (24%), 16 (21%), and 30 (38%) were significant (P < .05) for differentiating NMI from MI urothelial carcinoma. Highest areas the under curve for differentiating grading and invasive properties were obtained by variance (0.761, P < .001) and correlation (0.798, P < .001) on venous-phase CT images.Texture analysis has the potential to distinguish LGUC and HGUC, or NMI from MI urothelial carcinoma, before surgery.
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