CUAJ – Original Research Organ Et Al Classification Tree for Predicting Malignancy in Small Renal Masses Classification Tree for the Prediction of Malignant Disease and the Prediction of Non- Diagnostic Biopsies in Patients with Small Renal Masses

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 R.E.N.A.L. 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 R.E.N.A.L. 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). R.E.N.A.L. 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, R.E.N.A.L. and PADUA scores were not able to predict malignancy. CUAJ – Original Research Organ et al Classification tree for predicting malignancy in small renal masses Introduction Renal cell carcinoma (RCC) is the third most common urologic malignancy. Its incidence has been increasing due to increased detection of small renal masses (SRMs, defined as <4 cm). The majority of SRMs are asymptomatic at diagnosis and have a non-aggressive behavior, however some (up to 6.0%) can present with metastasis. Curative therapy including partial and radical nephrectomy are associated with considerable morbidity. Small renal masses are found to be histologically benign in 20-46% of cases. This prompted investigation into methods to predict benign vs. malignant disease. We have previously developed a classification tree (CT) using patient and tumour characteristics (tumour volume, location, and symptoms) to predict benign vs. malignant disease with an overall accuracy of 89%.(Figure 1) Two nomograms have been published that can be used as tools to predict surgical histology. These are complex and have not been widely adopted into clinical practice. It has been suggested that the R.E.N.A.L. scoring, which was designed to predict complications after partial nephrectomy, can predict benign from malignant disease, with more complex lesions having a greater malignant potential. The aims of this study were to externally validate our previously published CT using patient and tumour characteristics, and create a new CT with a combined cohort from two different institutions to predict histology using tumour size, location, symptoms and patient gender. Furthermore, the R.E.N.A.L. and PADUA scoring systems were evaluated to determine if they could predict malignancy or non-diagnostic biopsies. Methods Local institutional review boards approved this study which includes 818 renal masses. 423 patients were treated at the Princess Margaret Cancer Centre (PM), Toronto, Canada and underwent a renal mass biopsy. 395 consecutive patients from QE2 Health Sciences Centre (QE2) in Halifax, Nova Scotia, Canada underwent surgical treatment for renal mass. The PM cohort was obtained from a prospectively maintained database of 423 patients with SRMs undergoing treatment or surveillance. All patients undergoing percutaneous SRM biopsy between January 2000 and December 2009 were eligible for inclusion in this study. Biopsies were performed for planning management (surveillance or intervention), at the time of thermal ablation or in the post-ablation period for suspicion of recurrence. The QE2 cohort included 395 patients who had open or laparoscopic partial or radical nephrectomy for renal masses ≤5cm between July 1, 2001 and June 30, 2010. Patients were identified from an institutionally maintained prospective database of patients with renal masses and from physician records. All patients were >18 years of age and had a renal mass with imaging characteristics consistent with RCC. Patients with renal angiomyolipoma were excluded. We used the combined PM+QE2 cohort to determine the validity of our previously published CT. We then used binary recursive partitioning analysis (RPA) to create the new, combined CT model for the prediction of tumour histology (benign vs. malignant), and to CUAJ – Original Research Organ et al Classification tree for predicting malignancy in small renal masses evaluate R.E.N.A.L. and PADUA scoring for the same. Potential patient prognostic factors used to develop the new CT included age, sex, and symptoms at diagnosis. Each renal mass was classified broadly as either benign or malignant. Pre-treatment images were reviewed for each renal mass. Potential radiographic predictive factors included tumour volume (three dimensions: V = 4 3 π xxx 8 ; two dimensions V = 4 3 π xx 8 (x+x) 2 ; and one dimension: V = 4 3 π x 3 8 , tumour location (central or peripheral), degree of endophytic component (1-100%), and tumour axis location. Tumour location was defined as central if the tumour was in direct contact with or invading the collecting system and/or renal sinus. All other renal masses were defined as peripheral. Degree of endophytic component was recorded as the percentage of the tumour that was within the normal contour of the kidney. Tumour axis location was designated according to three renal axes: 1) upper pole, interpolar, or lower pole, 2) medial or lateral and 3) anterior or posterior. Binary RPA implemented via classification and regression tree (CART) methodology is a semiparametric modeling algorithm that generates simply understood binary decision tree models that stratify cases into various risk categories according to several prognostic factors. From the perspective of knowledge translation, a CT is appealing as a clinical decision tool as it is simply represented and implemented as a set of binary decision rules. The CT models predict malignant or benign outcome as a function of a set of explanatory variables. The CT models were developed automatically using the rpart package in the R language for statistical computing. Sensitivity, specificity, accuracy, and positive and negative predictive values were computed for the CT models. For the new CT, a random forest was utilized. Random forests is an ensemble method that constructs a collection of CTs with strategically injected variation by combining the concepts of bagging and the random selection of features. Random forests are effective at generating internal unbiased estimates of the generalization error. While random forests are difficult to interpret because they combine many models together, they typically outperform any single model generated by CART analysis. The random forest model was developed using the randomForest package in the R language for statistical computing. Sensitivity, specificity, accuracy, and positive and negative predictive values were obtained from the confusion matrix generated by the random forest model. Results The total combined cohort sample was 818 patients. Renal biopsy was initially performed in 423 PM cohort patients, 278 (66%) of which were male. 288 (68%) masses were detected incidentally. The median tumour volume was 8.7 cm, while the median endophytic component was 50%. Only 357 (84% of the total 423 PM cohort masses) who underwent a diagnostic biopsy were included in the cohort and 281 (79%) of these revealed malignancy, the majority of which were clear cell RCC. Fourty-three participants with unknown symptomatology from both PM and QE2 cohorts were excluded from the combined cohort further reducing the participants to a CUAJ – Original Research Organ et al Classification tree for predicting malignancy in small renal masses final number of 709. (See Table 1 for further demographic data). R.E.N.A.L. and PADUA scores were also calculated for all the masses in the biopsy series. As previously reported, the old CT demonstrated an accuracy of 89%. When externally validated with the biopsy cohort (PM), the accuracy decreased to 74% (95% CI; 0.69-0.78). The QE2 and PM patients were then combined and the new CT was developed, which demonstrated an accuracy of 87% (95% CI; 0.84-0.89). As this new CT was not externally validated with a separate cohort, a random forest technique was used to create a more robust CT. (Figure 2) Using the CART methodology, the R.E.N.A.L. nephrometry score alone was not able to predict whether the SRM was benign or malignant. When looking individually at the components of the R.E.N.A.L. score, “nearness to the collecting system” was associated with malignancy, with those within 7mm of the collecting system (N scores 2 and 3) having a higher rate of malignancy (30%) than those further away (15%). Alone, size of the tumour was not predictive of malignancy, but when used with “nearness to the collecting system” in the form of a CT, larger masses (R scores 2 and 3) were more likely to be malignant. Neither the overall PADUA score or the individual components were predictive of malignancy. (Figure 3) Discussion Currently SRMs are being over-treated as not all are malignant. Pre-operative prediction of malignancy in SRMs is a significant challenge as there is currently no validated, non-invasive way to predict benign vs. malignant renal disease. Few groups have developed predictive tools to determine the risk of benign vs. malignant disease for SRMs. Lane et al. used age, gender, radiological size at diagnosis, symptoms at presentation, and smoking history to develop a nomogram. Their nomogram predicted benign SRM with an AUC of 64.4%. Kutikov et al. developed nomograms using the R.E.N.A