ArticleFrontiers in oncology2022
An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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8 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- 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
- 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 Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Preoperative Prediction of TACE Refractoriness in Hepatocellular Carcinoma Using CT-Based Radiomics Model.Journal of hepatocellular carcinoma · 2026Article
- Article
- Multisequence combined magnetic resonance imaging radiomics model to noninvasively predict nuclear grade of clear cell renal cell carcinoma: interpretable model development.Revista da Associacao Medica Brasileira (1992) · 2025Article
- Development and validation of intravoxel incoherent motion diffusion weighted imaging-based model for preoperative distinguishing nuclear grade and survival of clear cell renal cell carcinoma complicated with venous tumor thrombus.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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7 authors.
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Abstract
Objectives: To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM). Methods: 175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test. Results: The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all Conclusion: The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.
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