A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma
| Autor: | Dong-Yue Wen, Xin Li, Ling Chen, Hong Yang, Hai-Biao Yan, Rong-Quan He, Peng Lin, Yun He, Gang Chen, Sheng-Hua Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Oncology medicine.medical_specialty Multivariate analysis Bladder Urothelial Carcinoma Urinary Bladder Sequencing data Radiogenomics Contrast Media 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Risk Factors Internal medicine medicine Humans Radiology Nuclear Medicine and imaging business.industry Weighted correlation network analysis General Medicine Nomogram Prognosis Precision medicine Survival Analysis Radiographic Image Enhancement Nomograms Urinary Bladder Neoplasms 030220 oncology & carcinogenesis Female Radiology Tomography X-Ray Computed Transcriptome business Selection operator |
| Zdroj: | European Radiology. 30:547-557 |
| ISSN: | 1432-1084 0938-7994 |
| Popis: | To 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. RNA 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. The 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. The 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. • 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink