ArticleCancer management and research2019
New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma.
Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Quantitative MRI for World Health Organisation/International Society of Urological Pathology Grading of Renal Cell Carcinoma: a systematic review and diagnostic meta-analysis.European radiology · 2026Article
- 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
- ANXA13 promotes cell proliferation and invasion and attenuates apoptosis in renal cell carcinoma.Heliyon · 2023Article
- 2-[European journal of nuclear medicine and molecular imaging · 2021Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveNuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI. MATERIALS AND
methodsThe data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CI-only model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients.
resultsThe fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5-80%. And the calibration curve showed good calibration in the training set and validation set.
conclusionThis study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist's evaluation patients' prognostic.
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