ArticleBioMed research international2020
A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- The Central Role of Imaging in Renal Cell Carcinoma: A Comprehensive Review of Tumor Aggressiveness, Histology, and Radiomics.Cancers · 2026Review
- CT radiomics for predicting postoperative disease specific survival in clear cell renal cell carcinoma: a multi-center study.Quantitative imaging in medicine and surgery · 2025Article
- Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Diagnostic and prognostic role ofEJNMMI research · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Article
- Imaging in Renal Cell Carcinoma Detection.Diagnostics (Basel, Switzerland) · 2024Review
- Renal cell carcinoma and venous tumor thrombus: predicting sarcomatoid dedifferentiation through preoperative IVIM-based MR imaging.Abdominal radiology (New York) · 2024Article
- Article
- CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children.BMC medical imaging · 2024Article
- The Use of Radiomic Tools in Renal Mass Characterization.Diagnostics (Basel, Switzerland) · 2023Review
- Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease.European radiology · 2023Article
- Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature.Cancers · 2023Review
- A nomogram based on the preoperative neutrophil-to-lymphocyte ratio to distinguish sarcomatoid renal cell carcinoma from clear cell renal cell carcinoma.Frontiers in oncology · 2023Article
- A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules.Frontiers in oncology · 2023Article
- Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.Central European journal of urology · 2023Review
- Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation.Abdominal radiology (New York) · 2022Article
- Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.Frontiers in medicine · 2022Article
- Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.Insights into imaging · 2021Article
- Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).Abdominal radiology (New York) · 2021Article
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4 authors.
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Abstract
This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤
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