ArticleJournal of magnetic resonance imaging : JMRI2019
A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.The British journal of radiology · 2022Trial
- Habitat-based radiomics-clinical analysis for early prediction of bladder cancer recurrence: a retrospective cohort study.Abdominal radiology (New York) · 2026Article
- Intratumoral and Peritumoral Fat CT‑Based Radiomics for Predicting Recurrence Risk in Non-Muscle-Invasive Bladder Cancer: A Two-Center Study.Annals of surgical oncology · 2026Article
- Recurrence risk prediction for non-muscle-invasive bladder urothelial carcinoma using diffusion and clinicopathology features.Abdominal radiology (New York) · 2025Article
- Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study.Journal of imaging · 2025Article
- Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review.Annals of surgical oncology · 2025Review
- Global research trends and hotspots on imaging of bladder cancer: A bibliometric and visual analysis from 1981 to 2023.Medicine · 2025Article
- Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer.Current urology · 2025Review
- 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
- Multiparametric MRI-based VI-RADS: can it predict 1- to 5-year recurrence of bladder cancer?European radiology · 2024Article
- Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.NPJ digital medicine · 2024Review
- Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics.Insights into imaging · 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
- Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Radiomics nomogram for predicting disease-free survival after partial resection or radical cystectomy in patients with bladder cancer.The British journal of radiology · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.EClinicalMedicine · 2023Article
- Review
- Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundPreoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. PURPOSE: To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. STUDY TYPE: Retrospective. POPULATION: Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). FIELD STRENGTH/SEQUENCE: 3.0T MRI/T ASSESSMENT: Radiomics features were extracted from the T STATISTICAL TESTS: Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction.
resultsOf the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone. DATA
conclusionThe proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904.
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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.