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Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.
- Source :
-
Aging [Aging (Albany NY)] 2021 Mar 26; Vol. 13 (7), pp. 9960-9975. Date of Electronic Publication: 2021 Mar 26. - Publication Year :
- 2021
-
Abstract
- Objectives: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC).<br />Methods: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics).<br />Results: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001).<br />Conclusions: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.
- Subjects :
- Aged
Algorithms
Carcinoma, Renal Cell genetics
Carcinoma, Renal Cell mortality
Carcinoma, Renal Cell pathology
DNA-Binding Proteins genetics
Female
Histone-Lysine N-Methyltransferase genetics
Humans
Imaging Genomics
Kidney Neoplasms genetics
Kidney Neoplasms mortality
Kidney Neoplasms pathology
Male
Middle Aged
Models, Theoretical
Neoplasm Grading
Nomograms
Prognosis
Survival Rate
Tomography, X-Ray Computed
Transcription Factors genetics
Tumor Suppressor Proteins genetics
Ubiquitin Thiolesterase genetics
Von Hippel-Lindau Tumor Suppressor Protein genetics
Carcinoma, Renal Cell diagnostic imaging
Kidney Neoplasms diagnostic imaging
Mutation
Subjects
Details
- Language :
- English
- ISSN :
- 1945-4589
- Volume :
- 13
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Aging
- Publication Type :
- Academic Journal
- Accession number :
- 33795526
- Full Text :
- https://doi.org/10.18632/aging.202752