ArticleFrontiers in oncology2021
Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- From Radiomics to Radiogenomics: Decoding Renal Cell Carcinoma Biology for Precision Medicine-a narrative review.Insights into imaging · 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
- Predicting first-line VEGFR-TKI resistance and survival in metastatic clear cell renal cell carcinoma using a clinical-radiomic nomogram.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.European journal of radiology open · 2023Article
- A review of radiomics and genomics applications in cancers: the way towards precision medicine.Radiation oncology (London, England) · 2022Review
- Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions.Cancers · 2022Review
- Review
- Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma.Frontiers in oncology · 2022Article
- Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2021Article
- Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma.Frontiers in oncology · 2021Article
- Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.
methodWe extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients.
resultsA total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814
conclusionIn this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.
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