ArticleAging2021
Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline 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
- CT-Based Radiomics for Prediction of Molecular Markers in Clear Cell Renal Cell Carcinoma: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of radiogenomics for risk stratification of intracranial aneurysms: a pilot study.Neuroradiology · 2025Article
- Colorectal cancer liver metastases: A radiologic point of view.World journal of gastrointestinal oncology · 2025Article
- Evaluation of Radiogenomics for Risk Stratification of Intracranial Aneurysms: A Pilot Study.Research square · 2025Article
- Targeting radiotherapy-induced inflammation in cancer metastasis: insights into immune modulation, therapeutic opportunities and radiogenomics.Frontiers in oncology · 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
- MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.BMC medical imaging · 2024Article
- Review
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- Advancements in Radiogenomics for Clear Cell Renal Cell Carcinoma: Understanding the Impact of BAP1 Mutation.Journal of clinical medicine · 2024Review
- Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma.Current issues in molecular biology · 2024Review
- 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
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.Reviews in cardiovascular medicine · 2023Article
- Integration of the Microbiome, Metabolome and Transcriptome RevealsAnimals : an open access journal from MDPI · 2023Article
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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).
methodsGenetic 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).
resultsUsing radiomics features, random forest algorithm showed good capacity to identify the mutations
conclusionsCT 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.