ArticleFrontiers in oncology2021
Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.Journal of imaging informatics in medicine · 2026Article
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Cancer classification with radiomics in controlled preclinical models.Scientific reports · 2026Article
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study.European radiology experimental · 2023Article
- A review of radiomics and genomics applications in cancers: the way towards precision medicine.Radiation oncology (London, England) · 2022Review
- Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.Gastroenterology · 2022Article
- The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model.Applied sciences (Basel, Switzerland) · 2022Article
- Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study.European radiology experimental · 2022Article
- Review
- Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.Tomography (Ann Arbor, Mich.) · 2021Article
- Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information.Journal of digital imaging · 2021Article
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10 authors.
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Abstract
purposeWe aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them. MATERIALS AND
methodsPreoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Non-parametric principal component decomposition (PCD) and unsupervised hierarchical clustering were applied to build the radiomics signature models. The factors confounding the radiomics signature were investigated and controlled sequentially. Kaplan-Meier curves and Cox regression analyses were performed to test the association between radiomics signatures and survival outcomes.
results183 patients of TCGA-KIRC cohort with available imaging, pathological, and clinical outcomes were included in this study. All 1,160 radiomics features were included in the first radiomics signature. Three additional radiomics signatures were then modelled in successive steps removing redundant radiomics features first, removing radiomics features biased by CT slice thickness second, and removing radiomics features dependent on tumor size third. The final radiomics signature model was the most parsimonious, unbiased by CT slice thickness, and independent of tumor size. This final radiomics signature stratified the cohort into radiomics phenotypes that are different by cancer-specific and recurrence-free survival; HR (95% CI) = 3.0 (1.5-5.7), p <0.05 and HR (95% CI) = 6.6 (3.1-14.1), p <0.05, respectively.
conclusionRadiomics signature can be confounded by multiple factors, including feature redundancy, image acquisition parameters like slice thickness, and tumor size. Attention to and proper control for these potential confounders are necessary for a reliable and clinically valuable radiomics signature.
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