ArticleJournal of oncology2021
A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images.
Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.The British journal of radiology · 2024Article
- Classification of nasal polyps and inverted papillomas using CT-based radiomics.Insights into imaging · 2023Article
- Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.Central European journal of urology · 2023Review
- Clinical-radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors.Oncology letters · 2022Article
- 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
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes.
methodsThe radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P).
resultsFor the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance.
conclusionsA reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.
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Registered trials
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