A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma.
Objectives: 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.Methods: RNA sequencing data, radiomics features and clinical parameters of 62...
| Publicado en: | European Radiology Vol. 30; no. 1; pp. 547 - 558 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140064756&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140064756 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2020 vid: 30 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140064756 140064756 NLM31396730 140064756 10.1007/s00330-019-06371-w NLM31396730 140064756 ppf: 547 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma. aug: au: Lin, Peng Wen, Dong-yue Chen, Ling Li, Xin Li, Sheng-hua Yan, Hai-biao He, Rong-quan Chen, Gang He, Yun Yang, Hong affil: Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China sug: subj: Models, Statistical Bladder Neoplasms Gene Expression Profiling Tomography, X-Ray Computed Methods Bladder Pathology Bladder Adult Prognosis Survival Analysis Bladder Neoplasms Pathology Female Risk Factors Contrast Media Radiographic Image Enhancement Methods Male Clinical Assessment Tools Scales Funding Source Human Adult: 19-44 years Female Male ab: Objectives: 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.Methods: 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.Results: 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.Conclusions: 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.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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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