ArticleJournal of digital imaging2021
Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review.World journal of urology · 2024Pooled it
- A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma.BMC medical imaging · 2025Trial
- The Central Role of Imaging in Renal Cell Carcinoma: A Comprehensive Review of Tumor Aggressiveness, Histology, and Radiomics.Cancers · 2026Review
- Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma.BMC cancer · 2026Article
- Radiomics-based tumor heterogeneity augments clinicopathological models for predicting recurrence in high-risk clear cell renal cell carcinoma after nephrectomy.Abdominal radiology (New York) · 2026Article
- Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.Scientific reports · 2025Article
- Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma.European urology open science · 2025Article
- Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.Scientific reports · 2025Article
- Article
- Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2024Article
- Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics.Heliyon · 2024Article
- Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization.Physics and imaging in radiation oncology · 2024Article
- Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features.Frontiers in medicine · 2024Article
- Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.La Radiologia medica · 2023Article
- 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
- Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study.Scientific reports · 2023Article
- CT radiomics prediction of CXCL9 expression and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study.Journal of digital imaging · 2023Article
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8 authors.
Funding
Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients' overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
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