Classification tree for the prediction of malignant disease and the prediction of non-diagnostic biopsies in patients with small renal masses
DOI:
https://doi.org/10.5489/cuaj.5196Keywords:
small renal masses, classification treeAbstract
Introduction: Preoperative prediction of benign vs. malignant small renal masses (SRMs) remains a challenge. This study: 1) validates our previously published classification tree (CT) with an external cohort; 2) creates a new CT with the combined cohort; and 3) evaluates the RENAL and PADUA scoring systems for prediction of malignancy.
Methods: This study includes a total of 818 patients with renal masses; 395 underwent surgical resection and 423 underwent biopsy. A CT to predict benign disease was developed using patient and tumour characteristics from the 709 eligible participants. Our CT is based on four parameters: tumour volume, symptoms, gender, and symptomatology. CART modelling was also used to determine if RENAL and PADUA scoring could predict malignancy.
Results: When externally validated with the surgical cohort, the predictive accuracy of the old CT dropped. However, by combining the cohorts and creating a new CT, the predictive accuracy increased from 74% to 87% (95% confidence interval 0.84–0.89). RENAL and PADUA score alone were not predictive of malignancy. One limitation was the lack of available histological data from the biopsy series.
Conclusions: The validated old CT and new combined-cohort CT have a predictive value greater than currently published nomograms and single-biopsy cohorts. Overall, RENAL and PADUA scores were not able to predict malignancy.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
You, the Author(s), assign your copyright in and to the Article to the Canadian Urological Association. This means that you may not, without the prior written permission of the CUA:
- Post the Article on any Web site
- Translate or authorize a translation of the Article
- Copy or otherwise reproduce the Article, in any format, beyond what is permitted under Canadian copyright law, or authorize others to do so
- Copy or otherwise reproduce portions of the Article, including tables and figures, beyond what is permitted under Canadian copyright law, or authorize others to do so.
The CUA encourages use for non-commercial educational purposes and will not unreasonably deny any such permission request.
You retain your moral rights in and to the Article. This means that the CUA may not assert its copyright in such a way that would negatively reflect on your reputation or your right to be associated with the Article.
The CUA also requires you to warrant the following:
- That you are the Author(s) and sole owner(s), that the Article is original and unpublished and that you have not previously assigned copyright or granted a licence to any other third party;
- That all individuals who have made a substantive contribution to the article are acknowledged;
- That the Article does not infringe any proprietary right of any third party and that you have received the permissions necessary to include the work of others in the Article; and
- That the Article does not libel or violate the privacy rights of any third party.