ArticleInternational journal of molecular sciences2023
MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Association of radiomic features with genomic signatures in thyroid cancer: a systematic review.Journal of translational medicine · 2024Pooled it
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- Diagnostic and Prognostic Non-Invasive Markers in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- MRI based unsuperviced clustering on MIBC reveals intratumor heterogeneity phenotypes and neoadjuvant chemotherapy efficacy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Review
- Emerging Molecular and Computational Biomarkers in Urothelial Carcinoma: Innovations in Diagnosis, Prognosis, and Therapeutic Response Prediction.Journal of personalized medicine · 2026Review
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Comparative Analysis of CT and MRI Combined with RNA Sequencing for Radiogenomic Staging of Bladder Cancer.International journal of molecular sciences · 2025Article
- Enhancing the breeding gene pool of wheat using accessions in gene banks as demonstrated by the Watkins collection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Recent Advances and Emerging Innovations in Transurethral Resection of Bladder Tumor (TURBT) for Non-Muscle Invasive Bladder Cancer: A Comprehensive Review of Current Literature.Research and reports in urology · 2025Review
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-staged. There is a pressing need for a more accurate staging modality to evaluate patients with bladder cancer to assist clinical decision-making. We hypothesize that MRI/RNA-seq-based radiogenomics and artificial intelligence can more accurately stage bladder cancer. A total of 40 magnetic resonance imaging (MRI) and matched formalin-fixed paraffin-embedded (FFPE) tissues were available for analysis. Twenty-eight (28) MRI and their matched FFPE tissues were available for training analysis, and 12 matched MRI and FFPE tissues were used for validation. FFPE samples were subjected to bulk RNA-seq, followed by bioinformatics analysis. In the radiomics, several hundred image-based features from bladder tumors in MRI were extracted and analyzed. Overall, the model obtained mean sensitivity, specificity, and accuracy of 94%, 88%, and 92%, respectively, in differentiating intra- vs. extra-bladder cancer. The proposed model demonstrated improvement in the three matrices by 17%, 33%, and 25% and 17%, 16%, and 17% as compared to the genetic- and radiomic-based models alone, respectively. The radiogenomics of bladder cancer provides insight into discriminative features capable of more accurately staging bladder cancer. Additional studies are underway.
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Registered trials
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