ArticleAbdominal radiology (New York)2021
Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Review
- The Central Role of Imaging in Renal Cell Carcinoma: A Comprehensive Review of Tumor Aggressiveness, Histology, and Radiomics.Cancers · 2026Review
- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- Machine learning based classification of aggressive and malignant renal tumors from multimodal data.PLOS digital health · 2026Article
- Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes.The Indian journal of radiology & imaging · 2025Article
- In Pursuit of KI-RADS: Toward a Single, Evidence-based Imaging Classification of Renal Masses.Radiology · 2025Review
- Multi-channel multiphase CT-based deep learning and radiomics fusion model for noninvasive pathological grading of clear cell renal cell carcinoma.Frontiers in oncology · 2025Article
- MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors.European journal of radiology open · 2024Article
- Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.Urolithiasis · 2024Article
- Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.Cancers · 2024Review
- The Use of Radiomic Tools in Renal Mass Characterization.Diagnostics (Basel, Switzerland) · 2023Review
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 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
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8 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs).
methodsCT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3-4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change.
resultsThe XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67.
conclusionsStatistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.
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