Objectives. Patients and Methods. Conclusion. Keywords. Results. Introduction

Transcription

1 predicts pathological nodal involvement for patients with renal cell carcinoma: development of a risk prediction model Boris Gershman*, Naoki Takahashi, Daniel M. Moreira*, Robert H. Thompson*, Stephen A. Boorjian*, Christine M. Lohse, Brian A. Costello, John C. Cheville and Bradley C. Leibovich* Departments of *Urology, Radiology, Health Sciences Research, Oncology, and Pathology, Mayo Clinic, Rochester, MN, USA Objectives To evaluate the ability of clinical and radiographic features to predict lymph node (pn1) disease among patients with renal cell carcinoma undergoing radical nephrectomy (RN), and to develop a preoperative risk prediction model. Patients and Methods In all, 220 patients with preoperative computed tomography scans available for review underwent RN with lymph node dissection (LND) from 2000 to Radiographic features were assessed by one genitourinary radiologist blinded to pn status. Associations of features with pn1 disease were evaluated using logistic regression to develop predictive models. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Results The median (interquartile range) lymph node yield was 10 (5 18). In all, 55 patients (25%) had pn1 disease at RN. On univariable analysis, the maximum lymph node (LN) short axis diameter [odds ratio (OR) 1.17; P < 0.001] predicted pn1 disease with an AUC of Although several clinical and radiographic features were associated with pn1 disease, only two were retained in the multivariable model: maximum LN short axis diameter (OR 1.19; P <0.001) and radiographic perinephric/sinus fat invasion (OR 44.64; P = 0.01), with an AUC of On decision curve analysis, the single variable and multivariable models showed similar net benefit. Conclusion Two radiographic features, maximum LN short axis diameter and perinephric/sinus fat invasion, outperformed traditional clinical variables in predicting pn1 disease. Maximum LN short axis diameter alone showed excellent predictive performance, and, if validated externally, would provide for a simple model to guide patient selection for LND. Keywords lymph node dissection, lymphadenopathy, renal cell carcinoma, nephrectomy, node positive Introduction Lymph node dissection (LND) for RCC has an established role in disease staging, but its impact on disease recurrence and survival has been the subject of debate [1,2]. While there are level 1 data to suggest that LND does not confer a therapeutic benefit in very-low-risk patients [3], there are observational data to support its use in patients at higher risk of harbouring nodal metastases [4 7]. As LND would in principle benefit only the latter group of patients, it is critical to preoperatively stratify patients for pathological nodal disease (pn1) risk in order to guide patient selection. Cross-sectional imaging alone has poor performance for detection of LN metastases when a traditional 1-cm threshold is used, with sensitivity of 40 83% and specificity of 82 92% [8 12]. Consequently, several groups have developed models to predict the risk of pn1 disease, although none has gained widespread adoption [13 15]. Our group has previously developed a scoring system based on five features to estimate a patient s probability of pn1 disease, including three that BJU Int 2016; 118: wileyonlinelibrary.com BJU International 2016 BJU International doi: /bju Published by John Wiley & Sons Ltd.

2 require intraoperative frozen section [13]. Two other groups have created nomograms based on preoperative features [14,15]. We hypothesised that preoperative radiographic imaging features may represent important predictors of pn1 disease and that independent review may enhance their utility in a risk prediction model. Accordingly, the purpose of the present study was twofold. First, we evaluated both established and novel independently reviewed radiographic features for predicting pn1 disease at radical nephrectomy (RN). Second, we developed a preoperative risk prediction model using both clinical and radiographic features to optimise model performance. Patients and Methods After obtaining Institutional Review Board approval, we queried the Mayo Clinic Nephrectomy Registry to identify consecutive patients who underwent RN for sporadic, unilateral, M0 or M1 RCC between 2000 and Of these, 517 patients underwent RN with concurrent LND, and 220 had preoperative, contrast-enhanced CT images available for review and formed the study cohort (Fig. S1). LND was performed at the surgeon s discretion, and a standardised template was not used. Comparisons of clinicopathological and chart-abstracted (unreviewed) radiographic features between those with and without available preoperative contrast-enhanced CTs are summarised in Table S1. Overall, the two groups were without significant differences for clinicopathological features, with the exception of a more recent year of surgery and higher LN yield for those with available CTs. Patients with available CTs had greater prevalence of several chartabstracted radiographic findings such as extra-renal extension and neovascularity, which may reflect subsequent efforts to obtain outside imaging. Clinicopathological and Radiographic Features Clinicopathological features recorded included year of surgery, age, gender, symptoms at presentation, smoking status, Eastern Cooperative Oncology Group performance status (ECOG PS), Charlson comorbidity index, body mass index (BMI), receipt of neoadjuvant therapy, surgical approach, number of LNs retrieved, stage according to the American Joint Committee on Cancer (AJCC) classification, pathological tumour size, histological subtype, nuclear grade, and presence of sarcomatoid differentiation or coagulative tumour necrosis. All pathology slides were re-reviewed by one genitourinary pathologist (J.C.C.). Preoperative CT scans were reviewed by one genitourinary radiologist (N.T.) who was blinded to the pn status of each patient. Radiographic features assessed included tumour size, perinephric/sinus fat invasion, extension beyond Gerota s fascia, renal vein or inferior vena cava (IVC) thrombus, tumour neovascularity, ill-defined tumour border, radiographic AJCC tumour (ct) stage, and presence of abdominal metastatic disease. In addition, for LNs measuring 7 mm in the short axis, we recorded number of enlarged LNs, short and long axis diameter, CT attenuation of the largest LN, and presence of necrosis. Chart-abstracted (unreviewed) radiographic features were recorded from original reports to evaluate correlation with radiographic review. These features included lymphadenopathy on CT, renal vein or IVC thrombus on CT or MRI, and radiographic evidence of haemorrhage, necrosis, calcification, extra-renal extension, neovascularity, adrenal involvement, and cystic or indeterminate cysts. Statistical Methods Comparisons of features stratified by pn status were evaluated using the Wilcoxon rank-sum and chi-square tests. Agreement between chart-abstracted and reviewed radiographic features was evaluated using Lin s concordance correlation coefficient and j statistics. Associations of clinical and reviewed radiographic features with pn1 status were evaluated using logistic regression and summarised with odds ratios (ORs) and 95% CIs. For patients with no LNs 7 mm in the short axis diameter, the radiographic features maximum LN short axis diameter and maximum LN long axis diameter were set to zero, CT attenuation was set to not assessed (NA), and LN necrosis was set to absent. As about half of the patients had LN CT attenuation set to NA, this feature was categorised so that the remaining patients were equally distributed into low- and high-attenuation categories. Loess plots indicated that age, BMI, and number of LNs retrieved should not be analysed as continuous features and were therefore categorised as follows: age, <65, 65 years; BMI, <25, 25 29, 30 34, 35 kg/m 2 ; number of LNs dissected, <13, 13. A multivariable model to predict pn1 status was developed using a bootstrap re-sampling approach with 500 samples and forward selection with the P value for a feature to enter the model set to Only those features that entered at least 70% of the models were retained in the final multivariable model [16]. Model discrimination was summarised using the area under the receiver operating characteristic curve (AUC), or c-index, which was internally validated using bootstrap resampling. Model calibration was summarised using the Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis was performed for both the best single-variable model and final-multivariable model [17] to quantify the net benefit of using model-predicted probabilities to guide BJU International 2016 BJU International 743

3 Gershman et al. performance of LND compared with a treat-all or treat-none approach. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC, USA) and R version (R Foundation for Statistical Computing, Vienna, Austria). All tests were two-sided with a P < 0.05 considered to indicate statistical significance. Results Clinicopathological and reviewed radiographic features for the 220 patients, including 54 (25%) who underwent cytoreductive nephrectomy for cm1 disease, are presented in Table 1. In all, 55 patients (25%) were pn1. The median [interquartile range (IQR)] LN yield was 10 (5 18), and notably, the extent of lymphadenectomy did not significantly differ by pn status (P = 0.58). The distribution of LN yield is depicted in Fig. S2. Patients with pn1 disease were more likely to have other adverse clinicopathological features, including symptomatic presentation, larger tumours, higher pt stage, high nuclear grade, and metastatic disease. In addition, patients with pn1 disease were more likely to have adverse radiographic features than pn0 patients, including a greater number of enlarged LNs, larger LN diameter, greater LN density, and LN necrosis, as well as tumour characteristics such as extension beyond Gerota s fascia, tumour neovascularity, ill-defined tumour border, presence/extent of tumour thrombus, and more advanced ct stage. Agreement between reviewed and corresponding chartabstracted radiographic features is shown in Table S2. There was poor agreement for cn status, even when the definition was varied from 7 to 10 mm in LN short axis diameter for the reviewed endpoint (j = ). The median (IQR) difference between reviewed and pathological tumour size was 0.6 ( 2.0 to 0.5) cm (Lin s concordance correlation coefficient = 0.75). Univariable associations of clinical and reviewed radiographic features with pn1 status are summarised in Table 2. Notably, the best univariable model consisted of maximum LN short axis diameter (OR 1.17 per mm; 95% CI ; P < 0.001), which alone predicted pn1 status with a bootstrapcorrected AUC of 0.84 (Hosmer-Lemeshow P = 0.46). For this single-variable model, the probability of pn1 disease is given by pn1 = e x /(1 + e x ), where x = * (maximum LN short axis diameter). For example, a patient with a maximum LN short axis diameter of 7 mm would have a predicted probability of Predicted probabilities of pn1 disease according to LN short axis diameter are presented in Fig. 1 and Table 3, and sensitivity/specificity at various diameters are depicted in the receiver operating characteristic curve in Fig. S3. As a comparison, chartabstracted radiographic lymphadenopathy had an AUC of We further examined whether the association of LN short axis diameter with pn1 disease varied according to cm1 status in a separate model that included the two variables and their interaction term. Notably, the interaction term was not significant (P = 0.54), indicating that this association was not different for patients undergoing cytoreductive nephrectomy or nephrectomy for cm0 disease. Although multiple clinical features were significantly associated with pn1 status on univariable analysis, only one feature retained an independent association after adjusting for maximum LN short axis diameter radiographic perinephric/ sinus fat invasion. The final multivariable model consisted of maximum LN short axis diameter (OR 1.19 per mm; 95% CI ; P < 0.001) and radiographic perinephric/sinus fat invasion (OR 44.64; 95% CI ; P = 0.01). This model predicted pn1 disease with an AUC of 0.85 (Hosmer-Lemeshow P = 0.55). The probability of pn1 disease is again given by pn1 =e x /(1 + e x ), where here x = *(maximum LN short axis diameter) (if perinephric/sinus fat invasion). Predicted probabilities of pn1 disease according to short axis diameter, stratified by radiographic perinephric/sinus fat invasion status, are given in Fig. 2. Lastly, we performed decision curve analysis to quantify the net benefit of using model predicted probabilities to guide patient selection for LND. Applying a risk-stratified approach based on model-predicted probabilities provided greater net benefit than treat-all/treat-none approaches for both the single-variable model (maximum LN short axis diameter) and the final multivariable model across threshold probabilities from 0.1 to 0.9 (Fig. 3). Moreover, both models provided similar net benefit, highlighting the clinical utility of maximum LN short axis diameter alone. Discussion The present study is the first, to our knowledge, to assess the predictive value of novel and established radiographic imaging features in identifying pn1 disease. Using these features, we developed two parsimonious risk-prediction models that may be used to guide patient selection for LND. The single most important predictor of pn1 disease was the size of retroperitoneal LNs, measured by maximum short axis diameter, with an AUC of Although many other clinical and radiographic features were significantly associated with pn1 disease on univariable analysis, including features reported in prior models such as ECOG PS, tumour size, and ctnm stage [14,15], none retained independent prognostic value aside from maximum LN short axis diameter and presence of radiographic perinephric/sinus fat invasion. Moreover, the multivariable model had marginally better discrimination (AUC 0.85) and similar net benefit on decision curve analysis compared with maximum LN short 744 BJU International 2016 BJU International

4 Table 1 Clinicopathological and reviewed radiographic features for patients who underwent RN with LND with available preoperative CT, stratified by pn status (N =220). Feature Overall cohort (N =220) pn0 (N =165) pn1 (N =55) P Clinicopathological Median (IQR): Year of surgery 2007 ( ) 2007 ( ) 2007 ( ) 0.17 Age at surgery, years 62 (55 69) 61 (55 70) 63 (54 68) 0.94 Charlson score 1 (0 6) 1 (0 3) 2 (0 6) 0.02 N (%) Male gender 148 (67) 110 (67) 38 (69) 0.74 ECOG PS 1 48 (22) 27 (16) 21 (38) <0.001 Smoking status (n =217) Never 78 (36) 59 (36) 19 (35) 0.88 Current 47 (22) 34 (21) 13 (24) Former 92 (42) 70 (43) 22 (41) BMI, kg/m 2 (n=219) 0.40 < (24) 40 (24) 13 (24) (37) 57 (35) 24 (44) (20) 33 (20) 11 (20) (19) 34 (21) 7 (13) Symptoms 157 (71) 111 (67) 46 (84) 0.02 Constitutional symptoms 72 (33) 44 (27) 28 (51) <0.001 Neoadjuvant treatment 9 (4) 4 (2) 5 (9) Laparoscopic approach 15 (7) 15 (9) Median (IQR) pathological tumour size, cm 9.5 ( ) 9.0 ( ) 11.5 ( ) <0.001 pt stage, n (%) <0.001 pt1a 5 (2) 5 (3) 0 pt1b 19 (9) 19 (12) 0 pt2a 30 (14) 26 (16) 4 (7) pt2b 20 (9) 16 (10) 4 (7) pt3a 103 (47) 78 (47) 25 (45) pt3b 29 (13) 17 (10) 12 (22) pt3c 1 (<1) 1 (1) 0 pt4 13 (6) 3 (2) 10 (18) Median (IQR) number of LN dissected (continuous) (n =214) 10 (5 18) 10 (5 18) 11 (2 19) 0.58 N (%) Number of LNs dissected (n =214) < (60) 103 (62) 26 (53) (40) 62 (38) 23 (47) 0.24 cm1 disease 54 (25) 32 (19) 22 (40) Histological subtype Clear cell 187 (85) 147 (89) 40 (73) <0.001 Papillary 10 (5) 3 (2) 7 (13) Chromophobe 12 (5) 10 (6) 2 (4) Clear cell papillary 3 (1) 3 (2) 0 Translocation-associated 1 (<1) 1 (1) 0 Collecting duct 1 (<1) 1 (1) 0 RCC not otherwise specified 6 (3) 0 6 (11) Nuclear grade 1 2 (1) 2 (1) 0 < (15) 34 (21) (51) 89 (54) 24 (44) 4 71 (32) 40 (24) 31 (56) Sarcomatoid differentiation 22 (10) 8 (5) 14 (25) <0.001 Coagulative tumour necrosis 134 (61) 84 (51) 50 (91) <0.001 Reviewed radiographic Median (IQR) radiographic tumour size, cm 9.0 ( ) 8.7 ( ) 9.7 ( ) N (%) Perinephric/sinus fat invasion 202 (92) 148 (90) 54 (98) Gerota s fascia involvement 27 (12) 14 (8) 13 (24) Ill-defined tumour border 125 (57) 80 (48) 45 (82) <0.001 Tumour neovascularity 126 (57) 87 (53) 39 (71) 0.02 Renal vein/ivc tumour thrombus None 129 (59) 105 (64) 24 (44) Renal vein 55 (25) 37 (22) 18 (33) IVC below diaphragm 29 (13) 20 (12) 9 (16) IVC above diaphragm 7 (3) 3 (2) 4 (7) Distant metastases in abdomen 28 (13) 13 (8) 15 (27) <0.001 BJU International 2016 BJU International 745

5 Gershman et al. Table 1 (continued) Feature Overall cohort (N =220) pn0 (N =165) pn1 (N =55) P ct stage ct1a 3 (1) 3 (2) 0 <0.001 ct1b 9 (4) 8 (5) 1 (2) ct2a 4 (2) 4 (2) 0 ct2b 2 (1) 2 (1) 0 ct3a 148 (67) 115 (70) 33 (60) ct3b 24 (11) 17 (10) 7 (13) ct3c 3 (1) 2 (1) 1 (2) ct4 27 (12) 14 (8) 13 (24) ct stage ct1 or ct2 18 (8) 17 (10) 1 (2) ct3a 148 (67) 115 (70) 33 (60) ct3b, ct3c, or ct4 54 (25) 33 (20) 21 (38) Number of LNs 7 mm (55) 112 (68) 10 (18) < (45) 53 (32) 45 (82) Median (IQR) Number of LNs 7 mm in short axis 0 (0 2) 0 (0 1) 3 (1 5) <0.001 Maximum LN short axis diameter, mm 0 (0 10) 0 (0 8) 17 (9 27) <0.001 Maximum LN long axis diameter, mm 0 (0 15) 0 (0 11) 23 (10 39) <0.001 N (%): CT attenuation of largest LN (HU) NA 122 (55) 112 (68) 10 (18) < (22) 20 (12) 29 (53) (22) 33 (20) 16 (29) LN necrosis 18 (8) 2 (1) 16 (29) <0.001 axis diameter alone, which underscores the relative importance of size of lymphadenopathy. A single-variable model has great clinical utility given ease of application. For instance, Fig. 1 may be used to guide preoperative decision making. Although a threshold probability for nodal involvement has not been identified for performance of LND, it is notable that even a LN short axis diameter of 7 mm was associated with a predicted 20% risk of nodal involvement. Practically, LN short axis diameter may enhance risk stratification beyond the traditional 1-cm threshold and allow clinicians to individualise patient management, taking into consideration the relative weight attributed to false-positive and false-negative results within the context of the oncological benefit of LND. Several additional observations bear further discussion. First, although the overall pn1 rate of 25% in the present study is higher than some previous studies [14], it reflects a contemporary, risk-adapted approach to performing LND consistent with recent reports [15]. Also, the extent of lymphadenectomy was relatively thorough as indicated by the distribution of LN yield, which should serve to minimise detection bias. Interestingly, measures of patient functional status, such as ECOG PS and symptomatic presentation, were significantly associated with pn1 disease, suggesting that systemic findings may be a manifestation of regionally advanced disease. Importantly, there was poor agreement between chart abstracted cn status and thresholds based on maximum LN short axis diameter, which underscores the potential heterogeneity in using unreviewed radiographic data. Furthermore, the performance improvement in using LN short axis diameter (AUC 0.84) rather than chart-abstracted radiographic cn status (AUC 0.72) illustrates the information loss that occurs with dichotomization of a continuous variable, and reinforces prior studies that demonstrated the poor performance of a traditional 1-cm size threshold for detection of LN metastases [8 12]. In an often-cited study on the accuracy of CT for assessing regional LN status, Studer et al. [8] reported that 58% of patients with radiographically enlarged LNs of >1 cm had benign pathology, or conversely, that only 42% of patients with LNs of >1 cm had pn1 disease. Despite major interval improvements in imaging, the median diameter of enlarged LNs in the study by Studer et al. [8] was 14 mm, almost exactly the diameter that corresponds to a 42% predicted probability of pn1 disease by our model (13.7 mm). Our present results indicate that size of lymphadenopathy is the single most important predictor of pn1 risk. Several groups have developed predictive models to estimate risk of nodal disease among patients undergoing nephrectomy [13 15,18,19]. Blute et al. [13] created a risk score using five pathological features, which was subsequently validated using intraoperative frozen section [18]. The features included: tumour size of 10 cm, stage pt3 or pt4, histological tumour necrosis, presence of sarcomatoid differentiation, and nuclear 746 BJU International 2016 BJU International

6 Table 2 Univariable associations of clinical and reviewed radiographic features with pn1 disease (N =220). Feature OR (95% CI) P Clinical Age 65 years (vs <65 years) 1.16 ( ) 0.64 Male gender 1.12 ( ) 0.74 Charlson score 1.17 ( ) ECOG PS ( ) Smoking status (n =217) Never 1.0 (reference) Current 1.19 ( ) 0.68 Former 0.98 ( ) 0.95 BMI, kg/m 2 (n=219) < (reference) ( ) ( ) ( ) 0.38 Symptoms 2.49 ( ) 0.02 Constitutional symptoms 2.85 ( ) Laparoscopic approach 0.09 ( ) 0.10 Year of surgery 0.93 ( ) Number of LNs retrieved (n =214) < (reference) ( ) 0.24 cm1 disease 2.77 ( ) Reviewed radiographic Radiographic tumour size (cm) 1.16 ( ) Perinephric or sinus fat invasion 6.20 ( ) 0.08 Gerota s fascia involvement 3.34 ( ) Ill-defined tumour border 4.78 ( ) <0.001 Tumour neovascularity 2.19 ( ) 0.02 Renal vein/inferior vena cava thrombus None 1.0 (reference) Renal vein 2.13 ( ) 0.04 Vena cava below diaphragm 1.97 ( ) 0.14 Vena cava above diaphragm 5.83 ( ) 0.03 Distant metastases in abdomen 4.39 ( ) <0.001 Radiographic tumour classification ct1a 1.51 ( ) ct1b ct2a ct2b ct3a ct3b ct3c ct4 Radiographic tumour classification ct1 or ct2 1.0 (reference) ct3a 4.88 ( ) 0.13 ct3b, ct3c, or ct ( ) 0.03 Number of LNs 7 mmin 2.02 ( ) 1 <0.001 short axis (continuous) At least one LN 7 mm in short axis 9.51 ( ) <0.001 Maximum LN short axis diameter (mm) 1.17 ( ) 1 <0.001 Maximum LN long axis diameter (mm) 1.12 ( ) 1 <0.001 CT attenuation of largest LN, HU NA 0.18 ( ) < ( ) (reference) LN necrosis ( ) < OR represents a 1-unit increase in the feature listed. grade 3 or 4, and patients with two or more features had a 20 50% incidence of pn1 disease [18]. Other groups have described preoperative nomograms with similar purpose. Hutterer et al. [19] developed a nomogram Fig. 1 Predicted probability (with 95% CIs) of pn1 disease according to maximum LN short axis diameter. For example, maximum LN short axis diameters of 7 mm, 10 mm, and 15 mm correspond to predicted probabilities of nodal involvement of 0.20, 0.29, and 0.47, respectively. Probability Maximum LN short axis diameter, mm Table 3 Predicted percentage of pn1 disease by maximum LN short axis diameter. Diameter, mm Percentage pn1 (95% CI) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) that included age, symptoms at presentation, and tumour size, although discrimination was only Capitanio et al. [14] likewise created a nomogram that included ctnm stage and tumour size, with a discrimination of Notably, extent of lymphadenectomy was not standardised, and the primary endpoint was a composite of pn1 disease (6.1%) or LN progression (4.1%) upon follow-up. Most recently, Babaian et al. [15] developed a nomogram that included ECOG PS, cn stage, presence of local symptoms, and lactate dehydrogenase (LDH), with a discrimination of Although these nomograms provide estimates using preoperatively available features, they are cumbersome to apply in clinical practice unless operationalised in some form. The present retrospective study has several limitations. Most importantly, there is inherent selection bias in the decision to perform LND, and our findings require external validation, BJU International 2016 BJU International 747

7 Gershman et al. Fig. 2 Predicted probability (with 95% CIs) of pn1 disease according to maximum LN short axis diameter, stratified by presence or absence of radiographic perinephric/sinus fat invasion. 1.0 Perinephric/Sinus Fat Invasion = Yes Perinephric/Sinus Fat Invasion = No 0.8 Probability Maximun LN short axis diameter, mm Fig. 3 Decision curve analysis for a risk-stratified approach to performing LND according to the maximum LN short axis diameter model or the final multivariable model (maximum LN short axis diameter and radiographic perinephric/sinus fat invasion) across various threshold probabilities, compared with treat-all and treat-none approaches. Net benefit Net benefit: Treat all Net benefit: Treat none Net benefit: Maximum LN short axis Net benefit: Multivariable model Threshold probability particularly in lower risk populations. However, the higherrisk cohort in the present study reflects a contemporary riskadapted approach to performance of LND and the target population to which a risk-stratification model would be applied. There is also potential selection bias in availability of preoperative CT studies for radiographic review, although comparison of clinicopathological characteristics in Table S1 revealed few clinically significant differences. In addition, the extent of LND was not standardised, and certain preoperative laboratory studies, such as neutrophil count or LDH, were not available for all patients. Finally, interobserver variability in radiographic interpretation may impact precision of risk stratification. In conclusion, we developed two parsimonious predictive models to estimate a patient s risk of pathological nodal disease at RN. Two radiographic features, maximum LN short axis diameter and presence of radiographic perinephric/sinus fat invasion, outperformed traditional clinical variables in predicting pn1 risk. Moreover, maximum LN short axis diameter alone demonstrated similar predictive performance to the multivariable model. If validated externally, the simplicity of such a model would facilitate clinical application to guide patient selection for LND. Conflicts of Interest The authors have no conflicts of interest to disclose. References 1 Capitanio U, Becker F, Blute ML et al. Lymph node dissection in renal cell carcinoma. Eur Urol 2011; 60: Motzer RJ, Agarwal N, Beard C et al. NCCN clinical practice guidelines in oncology: kidney cancer. J Natl Compr Canc Netw 2009; 7: Blom JH, van Poppel H, Marechal JM et al. Radical nephrectomy with and without lymph-node dissection: final results of European Organization for Research and Treatment of Cancer (EORTC) randomized phase 3 trial Eur Urol 2009; 55: Delacroix SE Jr, Chapin BF, Chen JJ et al. Can a durable disease-free survival be achieved with surgical resection in patients with pathological node positive renal cell carcinoma? J Urol 2011; 186: Pantuck AJ, Zisman A, Dorey F et al. Renal cell carcinoma with retroperitoneal lymph nodes. Impact on survival and benefits of immunotherapy. Cancer 2003; 97: Vasselli JR, Yang JC, Linehan WM, White DE, Rosenberg SA, Walther MM. Lack of retroperitoneal lymphadenopathy predicts survival of patients with metastatic renal cell carcinoma. J Urol 2001; 166: Whitson JM, Harris CR, Reese AC, Meng MV. Lymphadenectomy improves survival of patients with renal cell carcinoma and nodal metastases. J Urol 2011; 185: BJU International 2016 BJU International

8 8 Studer UE, Scherz S, Scheidegger J et al. Enlargement of regional lymph nodes in renal cell carcinoma is often not due to metastases. J Urol 1990; 144: Catalano C, Fraioli F, Laghi A et al. High-resolution multidetector CT in the preoperative evaluation of patients with renal cell carcinoma. AJR Am J Roentgenol 2003; 180: Ergen FB, Hussain HK, Caoili EM et al. MRI for preoperative staging of renal cell carcinoma using the 1997 TNM classification: comparison with surgical and pathologic staging. AJR Am J Roentgenol 2004; 182: Johnson CD, Dunnick NR, Cohan RH, Illescas FF. Renal adenocarcinoma: CT staging of 100 tumors. AJR Am J Roentgenol 1987; 148: Turkvatan A, Akdur PO, Altinel M et al. Preoperative staging of renal cell carcinoma with multidetector CT. Diagn Interv Radiol 2009; 15: Blute ML, Leibovich BC, Cheville JC, Lohse CM, Zincke H. A protocol for performing extended lymph node dissection using primary tumor pathological features for patients treated with radical nephrectomy for clear cell renal cell carcinoma. J Urol 2004; 172: Capitanio U, Abdollah F, Matloob R et al. When to perform lymph node dissection in patients with renal cell carcinoma: a novel approach to the preoperative assessment of risk of lymph node invasion at surgery and of lymph node progression during follow-up. BJU Int 2013; 112: E Babaian KN, Kim DY, Kenney PA et al. Preoperative predictors of pathological lymph node metastasis in patients with renal cell carcinoma undergoing retroperitoneal lymph node dissection. J Urol 2015; 193: Sauerbrei W, Schumacher M. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med 1992; 11: Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: Crispen PL, Breau RH, Allmer C et al. Lymph node dissection at the time of radical nephrectomy for high-risk clear cell renal cell carcinoma: indications and recommendations for surgical templates. Eur Urol 2011; 59: Hutterer GC, Patard JJ, Perrotte P et al. Patients with renal cell carcinoma nodal metastases can be accurately identified: external validation of a new nomogram. Int J Cancer 2007; 121: Correspondence: Bradley C. Leibovich, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Leibovich.Bradley@mayo.edu Abbreviations: AJCC, American Joint Committee on Cancer; AUC, area under the receiver operating characteristic curve; BMI, body mass index; ECOG PS, Eastern Cooperative Oncology Group performance status; IQR, interquartile range; IVC, inferior vena cava; LDH, lactate dehydrogenase; LN(D), lymph node (dissection); NA, not assessed; OR, odds ratio; RN, radical nephrectomy. Supporting Information Additional Supporting Information may be found in the online version of this article: Fig. S1 Consolidated Standards Of Reporting Trials (CONSORT) diagram. Fig. S2 Distribution of number of LNs retrieved. Fig. S3 Receiver operating characteristic curve for pn1 disease against LN short axis diameter. Labels next to threshold points indicate LN short axis diameters. Table S1 Comparison of clinicopathological and chartabstracted radiographic features between patients with and without available preoperative, contrast-enhanced CT scans (N = 517). Table S2 Agreement between chart-abstracted (unreviewed) and reviewed radiographic features (N = 220). BJU International 2016 BJU International 749