Editorial for “Multiparametric MRI‐Based Deep Learning Radiomics Model for Assessing 5‐Year Recurrence Risk in Non‐Muscle Invasive Bladder Cancer”
Author:
Affiliation:
1. Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Medicine John's Hopkins University Baltimore Maryland USA
Publisher
Wiley
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.29592
Reference10 articles.
1. Epidemiology of Bladder Cancer
2. T1-Staging for Urinary Bladder Cancer with the Stalk and Inchworm Signs with 3.0 Tesla MRI
3. Recurrence and Progression of Disease in Non–Muscle-Invasive Bladder Cancer: From Epidemiology to Treatment Strategy
4. Novel nomograms to predict recurrence and progression in primary non-muscle-invasive bladder cancer: validation of predictive efficacy in comparison with European Organization of Research and Treatment of Cancer scoring system
5. EORTC Nomograms and Risk Groups for Predicting Recurrence, Progression, and Disease-specific and Overall Survival in Non–Muscle-invasive Stage Ta–T1 Urothelial Bladder Cancer Patients Treated with 1–3 Years of Maintenance Bacillus Calmette-Guérin
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