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A classification tree for the prediction of benign versus malignant disease in patients with small renal masses
Rendon A. Ricardo; Mason J. Ross; Kirkland Susan; Lawen G. Joseph; Abdolell Mohamed; Dalhousie University and Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
Aug 2014 (Vol. 21, Issue 4, Pages( 7379 - 7384)
PMID: 25171283

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  • INTRODUCTION: To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses. MATERIALS AND METHODS: This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass < 5 cm in maximum diameter between July 1st 2001 and June 30th 2010. A classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%-100%), and tumor axis position were used as potential predictors to develop the model. RESULTS: Forty-five patients (11.4%) were found to have a benign mass postoperatively. A classification tree has been developed which can predict the risk of benign disease with an accuracy of 88.9% (95% CI: 85.3 to 91.8). The significant prognostic factors in the classification tree are tumor volume, degree of endophytic component and symptoms at diagnosis. As an example of its utilization, a renal mass with a volume of < 5.67 cm3 that is < 45% endophytic has a 52.6% chance of having benign pathology. Conversely, a renal mass with a volume ≥ 5.67 cm3 that is ≥ 35% endophytic has only a 5.3% possibility of being benign. CONCLUSIONS: A classification tree to predict the risk of benign disease in small renal masses has been developed to aid the clinician when deciding on treatment strategies for small renal masses.

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