European Radiology

, Volume 30, Issue 1, pp 547–557 | Cite as

A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma

  • Peng Lin
  • Dong-yue Wen
  • Ling Chen
  • Xin Li
  • Sheng-hua Li
  • Hai-biao Yan
  • Rong-quan He
  • Gang Chen
  • Yun He
  • Hong YangEmail author
Imaging Informatics and Artificial Intelligence



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.

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.


Multidetector computed tomography Urinary bladder neoplasms Artificial intelligence Prognosis 



Area under the curve


Bladder urothelial carcinoma


Contrast-enhanced computed tomography


Hazard ratio


Kyoto Encyclopedia of Genes and Genomes


Least absolute shrinkage and selection operator


Progression-free interval


Receiver operating characteristic


The regions of interest


The Cancer Genome Atlas


The Cancer Imaging Archive


Trans per million


Weighted gene co-expression network analysis



The authors would like to thank the TCGA and TCIA databases for the availability of the data.


This study has received funding by grants from the Guangxi Science and Technology Program (grant no. GuiKeAB17195020), the Fund of National Natural Science Foundation of China (grant no. NSFC81260222) and Innovation Project of Guangxi Graduate Education (grant no. YCSW2018104).

Compliance with ethical standards


The scientific guarantor of this publication is Hong Yang.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors (Ling Chen) has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas Bladder Urothelial Carcinoma [TCGA-BLCA]).

Ethical approval

Institutional Review Board approval was not required because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas-Bladder Urothelial Carcinoma [TCGA-BLCA]).


• retrospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Peng Lin
    • 1
  • Dong-yue Wen
    • 1
  • Ling Chen
    • 2
  • Xin Li
    • 2
  • Sheng-hua Li
    • 3
  • Hai-biao Yan
    • 3
  • Rong-quan He
    • 4
  • Gang Chen
    • 5
  • Yun He
    • 1
  • Hong Yang
    • 1
    Email author
  1. 1.Department of Medical UltrasonicsFirst Affiliated Hospital of Guangxi Medical UniversityNanningPeople’s Republic of China
  2. 2.GE HealthcareShanghaiChina
  3. 3.Department of UrologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningPeople’s Republic of China
  4. 4.Department of Medical OncologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningPeople’s Republic of China
  5. 5.Department of PathologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningPeople’s Republic of China

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