1201 EXTERNAL VALIDATION AND CREATION OF A NEW CLASSIFICATION TREE FOR THE PREDICTION OF BENIGN VERSUS MALIGNANT DISEASE IN PATIENTS WITH SMALL RENAL MASSES

2013 ◽  
Vol 189 (4S) ◽  
Author(s):  
Michael Organ ◽  
Michael Jewett ◽  
Ashraf Almatar ◽  
Henry Ajzenberg ◽  
Mohamed Abdolell ◽  
...  
2018 ◽  
Vol 13 (4) ◽  
Author(s):  
Michael Organ ◽  
Landan P. MacDonald ◽  
Michael A.S. Jewett ◽  
Henry Ajzenberg ◽  
Ashraf Almatar ◽  
...  

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.


2013 ◽  
Vol 189 (4S) ◽  
Author(s):  
Michael Organ ◽  
Michael Jewett ◽  
Ashraf Almatar ◽  
Henry Ajzenberg ◽  
Mohamed Abdolell ◽  
...  

2019 ◽  
Vol 14 (5) ◽  
Author(s):  
Charlie J. Gillis ◽  
Ricardo Rendon ◽  
Landan P. MacDonald ◽  
Michael A.S. Jewett ◽  
Christopher French ◽  
...  

Introduction: As greater numbers of small renal masses (SRMs) are discovered incidentally, renal tumor biopsy (RTB) is an increasingly recognized step for the management of these lesions, ideally for the prevention of surgical overtreatment for benign disease. While the diagnosis can often be obtained preoperatively by RTB, indeterminate results create greater difficulty for patients and clinicians. This study examines a series of RTBs, identifying the portion of these that were able to yield a diagnosis, and correlates patient factors, including RENAL and PADUA scoring, with the outcome of a non-diagnostic result. Methods: Patients were identified as having undergone RTB at the Princess Margaret Cancer Centre in Ontario, Canada, between January 2000 and December 2009. Data was compiled from these 423 patients and analyzed using CART methodology to determine the level of association between various patient and tumor factors and the outcome of a non-diagnostic biopsy. Tumor size was further used to develop a classification tree to describe the prediction of a non-diagnostic biopsy. Results: Of these 423 patients undergoing RTB, 66 (16%) resulted in a non-diagnostic biopsy. The only patient or tumor factor that was found to be associated with a non-diagnostic outcome was mass size, where small masses (<1.28 cm diameter) were found to have a 38% chance of being non-diagnostic, compared with a 13% chance in those tumors >1.28 cm diameter (86% accuracy, 95% confidence interval [CI] 0.82–0.89). Conclusions: When evaluating SRMs for diagnostic workup, mass size is the only tumor or patient characteristic associated with a non-diagnostic RTB.


2012 ◽  
Vol 188 (6) ◽  
pp. 2072-2076 ◽  
Author(s):  
Jeffrey K. Mullins ◽  
Jihad H. Kaouk ◽  
Sam Bhayani ◽  
Craig G. Rogers ◽  
Michael D. Stifelman ◽  
...  

2014 ◽  
Vol 191 (4S) ◽  
Author(s):  
Michael Organ ◽  
Ross Mason ◽  
Mohamed Abdolell ◽  
Ricardo Rendon

2020 ◽  
Vol 23 (2) ◽  
pp. 100674
Author(s):  
Mohamed E. Abdelsalam ◽  
Kamran Ahrar

2006 ◽  
Vol 175 (4S) ◽  
pp. 229-229
Author(s):  
David C. Miller ◽  
John M. Hollingsworth ◽  
Khaled S. Hafez ◽  
Stephanie Daignault ◽  
Brent K. Hollenbeck

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