ArticleEuropean radiology2020
CT-based radiomics to predict the pathological grade of bladder cancer.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic performance of artificial intelligence in detecting bladder carcinoma.World journal of urology · 2025Pooled it
- Multi-DECT image-based intra- and peritumoral interpretable radiomics and artificial intelligence models for preoperative prediction of pathological grading of non-muscle-invasive bladder cancer.Abdominal radiology (New York) · 2026Article
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.European urology open science · 2026Article
- Radiomics-Based Fundus Photography Analysis in Diabetic Retinopathy.Translational vision science & technology · 2026Article
- CT image-derived radiomics predicts molecular subtypes in bladder urothelial carcinoma: validation of a non-invasive classification strategy.Scientific reports · 2026Article
- Leveraging vision transformer for histological grade prediction in laryngeal and hypopharyngeal squamous cell carcinoma: a large-scale multicenter study.Neuroradiology · 2026Article
- Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study.Military Medical Research · 2026Article
- Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis.Cancer biology & medicine · 2025Article
- Development of a radiomics and clinical feature-based nomogram for preoperative prediction of pathological grade in bladder cancer.Frontiers in oncology · 2025Article
- A Machine Learning Model Based on Multi-Phase Contrast-enhanced CT for the Preoperative Prediction of the Muscle-Invasive Status of Bladder Cancer.Current medical imaging · 2025Article
- Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma.BMC cancer · 2024Article
- 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
- Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging.BMC medical imaging · 2024Article
- Preoperative Prediction of Muscle Invasiveness in Bladder Cancer: The Role of 3D Volumetric Radiomics Using Diffusion-Weighted MRI, the VI-RADS Score, or a Combination of Both.Annals of surgical oncology · 2024Article
- Quantitative CT Morphometrics: A Novel Approach for Predicting the Bladder Cancer Grade.Cureus · 2024Article
- Prognostic predictive value of urothelial carcinoma of the bladder after TURBT based on multiphase CT radiomics.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
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily.
methodsPatients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
resultsOut of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912-0.988) in the training group and 0.860 (95% CI 0.742-0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively.
conclusionsCT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance. KEY POINTS: •CT-based radiomics model can predict the pathological grade of bladder cancer. •This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer. •This preoperative and non-invasive prediction method might become an important addition to biopsy.
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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.