ArticleInsights into imaging2024
Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Radiomics as a tool for predicting biochemical recurrence after total prostatectomy: a systematic review and meta-analysis.International urology and nephrology · 2026Pooled it
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- 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
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Recurrence risk prediction for non-muscle-invasive bladder urothelial carcinoma using diffusion and clinicopathology features.Abdominal radiology (New York) · 2025Article
- A Composite Risk Score Based on VI-RADS, Tumor Contact Length, and CYFRA 21-1 for Prognostic Stratification in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics-based machine-learning method to predict extrahepatic metastasis in hepatocellular carcinoma after hepatectomy: a multicenter study.Scientific reports · 2025Article
- Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?Abdominal radiology (New York) · 2025Review
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer.Journal of thoracic disease · 2025Article
- Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment.World journal of gastrointestinal surgery · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveWe aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in bladder cancer (BCa) patients and assess its superiority over clinical models.
methodsA retrospective cohort of 229 BCa patients with preoperative multi-sequence MRI was divided into a training set (n = 160) and a validation set (n = 69). Radiomics features were extracted from T2-weighted images, diffusion-weighted imaging, apparent diffusion coefficient, and dynamic contrast-enhanced images. Effective features were identified using the least absolute shrinkage and selection operator (LASSO) method. Clinical risk factors were determined via univariate and multivariate Cox analysis, leading to the creation of a radiomics-clinical nomogram. Kaplan-Meier analysis and log-rank tests assessed the relationship between radiomics features and RFS. We calculated the net reclassification improvement (NRI) to evaluate the added value of the radiomics signature and used decision curve analysis (DCA) to assess the nomogram's clinical validity.
resultsRadiomics features significantly correlated with RFS (log-rank p < 0.001) and were independent of clinical factors (p < 0.001). The combined model, incorporating radiomics features and clinical data, demonstrated the best prognostic value, with C-index values of 0.853 in the training set and 0.832 in the validation set. Compared to the clinical model, the radiomics-clinical nomogram exhibited superior calibration and classification (NRI: 0.6768, 95% CI: 0.5549-0.7987, p < 0.001).
conclusionThe radiomics-clinical nomogram, based on multi-sequence MRI, effectively assesses the BCa recurrence risk. It outperforms both the radiomics model and the clinical model in predicting BCa recurrence risk. CRITICAL RELEVANCE STATEMENT: The radiomics-clinical nomogram, utilizing multi-sequence MRI, holds promise for predicting bladder cancer recurrence, enhancing individualized clinical treatment, and performing tumor surveillance. KEY POINTS: • Radiomics plays a vital role in predicting bladder cancer recurrence. • Precise prediction of tumor recurrence risk is crucial for clinical management. • MRI-based radiomics models excel in predicting bladder cancer recurrence.
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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.