ArticleEuropean radiology2020
A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma.
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 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Development and validation of the ultrasound-based radiomics and deep learning prognostic models for diffuse large B-cell lymphoma.Annals of hematology · 2026Article
- 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
- Comparative Analysis of CT and MRI Combined with RNA Sequencing for Radiogenomic Staging of Bladder Cancer.International journal of molecular sciences · 2025Article
- Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?Abdominal radiology (New York) · 2025Review
- 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
- Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer.Current urology · 2025Review
- 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
- Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme.Heliyon · 2024Article
- Validation of the 2018 FIGO staging system for stage IIIC cervical cancer by determining the metabolic and radiomic heterogeneity of primary tumors based onAbdominal radiology (New York) · 2024Article
- Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.BMC medical imaging · 2024Article
- MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.European radiology · 2024Article
- Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.Frontiers in oncology · 2024Article
- A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2023Article
- Article
- Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions.Current oncology (Toronto, Ont.) · 2023Review
- Article
- Development of a Molecular-Subtype-Associated Immune Prognostic Signature That Can Be Recognized by MRI Radiomics Features in Bladder Cancer.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo determine the integrative value of contrast-enhanced computed tomography (CECT), transcriptomics data and clinicopathological data for predicting the survival of bladder urothelial carcinoma (BLCA) patients.
methodsRNA sequencing data, radiomics features and clinical parameters of 62 BLCA patients were included in the study. Then, prognostic signatures based on radiomics features and gene expression profile were constructed by using least absolute shrinkage and selection operator (LASSO) Cox analysis. A multi-omics nomogram was developed by integrating radiomics, transcriptomics and clinicopathological data. More importantly, radiomics risk score-related genes were identified via weighted correlation network analysis and submitted to functional enrichment analysis.
resultsThe radiomics and transcriptomics signatures significantly stratified BLCA patients into high- and low-risk groups in terms of the progression-free interval (PFI). The two risk models remained independent prognostic factors in multivariate analyses after adjusting for clinical parameters. A nomogram was developed and showed an excellent predictive ability for the PFI in BLCA patients. Functional enrichment analysis suggested that the radiomics signature we developed could reflect the angiogenesis status of BLCA patients.
conclusionsThe integrative nomogram incorporated CECT radiomics, transcriptomics and clinical features improved the PFI prediction in BLCA patients and is a feasible and practical reference for oncological precision medicine. KEY POINTS: • Our radiomics and transcriptomics models are proved robust for survival prediction in bladder urothelial carcinoma patients. • A multi-omics nomogram model which integrates radiomics, transcriptomics and clinical features for prediction of progression-free interval in bladder urothelial carcinoma is established. • Molecular functional enrichment analysis is used to reveal the potential molecular function of radiomics signature.
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