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THE IMPACT OF POSITIVE SURGICAL MARGINS ON MORTALITY FOLLOWING RADICAL PROSTATECTOMY
THE JOURNAL OF UROLOGY®
Vol. 181, No. 4, Supplement, Sunday, April 26, 2009
PSM ranged from 0-100% amongst a total of 43 hospitals (Figure 1). The volume of radical prostatectomies ranged from 2 to 95 per hospital (median 37). There was no significant correlation between volume and PSM (R = -0.208, p = 0.1798). CONCLUSIONS: The province-wide PSM rate for pathological stage T2 disease prostate cancer undergoing radical prostatectomy is higher than published centres of excellence. A volume outcome relationship could not be established which contradicts previously published data. Factors such as individual surgeon, patient selection, pathological processing and interpretation may explain differences.
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adjuvant therapy, given the potential morbidity and costs of secondary treatments. Source of Funding: None
472 COMPARISON OF RISK STRATIFICATION MODELS AND THE KATTAN PREOPERATIVE NOMOGRAM IN PREDICTION OF FREEDOM FROM PSA RELAPSE AFTER RADICAL PROSTATECTOMY Carvell T Nguyen*, Adrian V Hernandez, Michael W Kattan, Andrew J Stephenson, J Stephen Jones, Eric A Klein, Cleveland, OH
Source of Funding: None
471 THE IMPACT OF POSITIVE SURGICAL MARGINS ON MORTALITY FOLLOWING RADICAL PROSTATECTOMY Stephen A Boorjian*, Rockledge, PA; R. Jeffrey Karnes, Paul L Crispen, Rachel Carlson, Laureano J Rangel, Eric J Bergstralh, Michael L Blute, Rochester, MN INTRODUCTION AND OBJECTIVE: Previous studies have demonstrated an association between positive surgical margins (PSM) at the time of radical prostatectomy (RP) and an increased risk of postoperative biochemical recurrence (BCR). However, the natural history of BCR is variable, and does not always translate into clinical progression or death from prostate cancer. Here, then, we evaluated the impact of PSM on the risks of local recurrence (LR), systemic progression (SP), cancer-specific survival (CSS), and overall survival (OS). METHODS: We reviewed 11,729 patients who underwent RP without prior therapy between 1990-2006. Survival was estimated using the Kaplan-Meier method and compared using the log rank test. Cox proportional hazard regression models were used to analyze the impact of PSM on postoperative survival. RESULTS: Overall, 3651 (31.1%) men were identified with a PSM, including 2072/8839 (23%) patients with organ-confined tumors and 1579/2868 (55%) patients with extracapsular disease. In 2615 (72%) cases, a single PSM was identified, while 1036 (28%) patients had q 2 PSM. A total of 454 (12%), 1075 (29%), 634 (17%), and 700 (19%) men who had a PSM received adjuvant radiation therapy (RT), adjuvant hormone therapy (HT), salvage RT, and salvage HT, respectively. At a median follow-up after surgery of 13.3 years (interquartile range 11.515.1), 3002 patients from the overall cohort had BCR, 678 experienced LR, 470 relapsed systemically, and 1731 died, with 274 dying of prostate cancer. The 10-year BCR-free rate for patients with and without a PSM was 56% and 77%, respectively (p<0.001), while the 10 year LRfree survival for patients with a PSM was 89%, compared to 95% for patients with a negative margin (p<0.001). PSM status also stratified 10-year SP-free survival (93% vs 97%, p<0.001), CSS (96% vs 99%, p<0.001), and OS (83% vs 88%, p<0.001). On multivariate analysis controlling for clinicopathological variables, however, the presence of a PSM was associated with increased risks of BCR (HR 1.3; 95% CI 1.2-1.4; p<0.001), LR (HR 1.4; 95% CI 1.1-1.7; p=0.005), and receipt of secondary treatment (HR 1.4; 95% CI 1.2-1.5; p<0.0001), but was not a significant predictor of SP (HR 0.91; 95% CI 0.7-1.2; p=0.46), CSS (HR 1.1; 95% CI 0.8-1.6; p=0.48), or OS (HR 1.1; 95% CI 0.92-1.2; p=0.37). CONCLUSIONS: The presence of a PSM increased the risks of BCR and LR following RP, but did not independently impact SP, CSS, or OS. These results should be considered when evaluating patients for
INTRODUCTION AND OBJECTIVE: A subset of men diagnosed with prostate cancer will have a greater risk of occult metastatic disease or recurrence following definitive therapy. Accurate identification of such patients is important for counseling, rational application of neoadjuvant/ adjuvant therapy, and the design and powering of clinical trials. Prediction tools generate estimates of clinical outcomes and can stratify patients by their predicted risk of disease recurrence. We compared the performance of 6 risk stratification models and the Kattan preoperative nomogram in predicting PSA relapse in high risk patients in order to determine the best tool for patient counseling. METHODS: We evaluated the performance of the 7 prediction tools in a population of men who underwent radical prostatectomy at the Cleveland Clinic from 1987 to 1995 (n=708) and 1996 to 2007 (n=3351). Freedom from biochemical relapse (FBR) at 5 years was our clinical endpoint. Performance of the 7 tools was evaluated based on discrimination (using c-index) and calibration. Trellis plots of nomogram-predicted 5-year FBR by each risk definition were performed to assess the degree of heterogeneity in the risk of PSA relapse within each high risk group. RESULTS: All 7 models discriminated better than random chance (Fig 1A). Multivariable models had superior discriminating ability with the Kattan nomogram showing the highest c-index. All 7 models demonstrated good calibration. There was significant heterogeneity within each of the 6 high risk groups with regards to predicted 5-year FBR rates when compared to their respective non-high risk counterparts (Fig 1B). CONCLUSIONS: These data confirm the value of using prediction tools to estimate outcomes in men with prostate cancer. However, patients are likely better served with individualized predictions that do not depend on group data. Despite incorporating the same preoperative variables, the Kattan nomogram was more accurate than any risk grouping in predicting PSA relapse in our patient cohort. Furthermore, given the considerable heterogeneity in the risk of PSA relapse in each of the 6 high risk definitions, risk stratification alone is not suitable for guiding treatment decisions for the individual patient.
Source of Funding: None
Vol. 181, No. 4, Supplement, Sunday, April 26, 2009
PSM ranged from 0-100% amongst a total of 43 hospitals (Figure 1). The volume of radical prostatectomies ranged from 2 to 95 per hospital (median 37). There was no significant correlation between volume and PSM (R = -0.208, p = 0.1798). CONCLUSIONS: The province-wide PSM rate for pathological stage T2 disease prostate cancer undergoing radical prostatectomy is higher than published centres of excellence. A volume outcome relationship could not be established which contradicts previously published data. Factors such as individual surgeon, patient selection, pathological processing and interpretation may explain differences.
169
adjuvant therapy, given the potential morbidity and costs of secondary treatments. Source of Funding: None
472 COMPARISON OF RISK STRATIFICATION MODELS AND THE KATTAN PREOPERATIVE NOMOGRAM IN PREDICTION OF FREEDOM FROM PSA RELAPSE AFTER RADICAL PROSTATECTOMY Carvell T Nguyen*, Adrian V Hernandez, Michael W Kattan, Andrew J Stephenson, J Stephen Jones, Eric A Klein, Cleveland, OH
Source of Funding: None
471 THE IMPACT OF POSITIVE SURGICAL MARGINS ON MORTALITY FOLLOWING RADICAL PROSTATECTOMY Stephen A Boorjian*, Rockledge, PA; R. Jeffrey Karnes, Paul L Crispen, Rachel Carlson, Laureano J Rangel, Eric J Bergstralh, Michael L Blute, Rochester, MN INTRODUCTION AND OBJECTIVE: Previous studies have demonstrated an association between positive surgical margins (PSM) at the time of radical prostatectomy (RP) and an increased risk of postoperative biochemical recurrence (BCR). However, the natural history of BCR is variable, and does not always translate into clinical progression or death from prostate cancer. Here, then, we evaluated the impact of PSM on the risks of local recurrence (LR), systemic progression (SP), cancer-specific survival (CSS), and overall survival (OS). METHODS: We reviewed 11,729 patients who underwent RP without prior therapy between 1990-2006. Survival was estimated using the Kaplan-Meier method and compared using the log rank test. Cox proportional hazard regression models were used to analyze the impact of PSM on postoperative survival. RESULTS: Overall, 3651 (31.1%) men were identified with a PSM, including 2072/8839 (23%) patients with organ-confined tumors and 1579/2868 (55%) patients with extracapsular disease. In 2615 (72%) cases, a single PSM was identified, while 1036 (28%) patients had q 2 PSM. A total of 454 (12%), 1075 (29%), 634 (17%), and 700 (19%) men who had a PSM received adjuvant radiation therapy (RT), adjuvant hormone therapy (HT), salvage RT, and salvage HT, respectively. At a median follow-up after surgery of 13.3 years (interquartile range 11.515.1), 3002 patients from the overall cohort had BCR, 678 experienced LR, 470 relapsed systemically, and 1731 died, with 274 dying of prostate cancer. The 10-year BCR-free rate for patients with and without a PSM was 56% and 77%, respectively (p<0.001), while the 10 year LRfree survival for patients with a PSM was 89%, compared to 95% for patients with a negative margin (p<0.001). PSM status also stratified 10-year SP-free survival (93% vs 97%, p<0.001), CSS (96% vs 99%, p<0.001), and OS (83% vs 88%, p<0.001). On multivariate analysis controlling for clinicopathological variables, however, the presence of a PSM was associated with increased risks of BCR (HR 1.3; 95% CI 1.2-1.4; p<0.001), LR (HR 1.4; 95% CI 1.1-1.7; p=0.005), and receipt of secondary treatment (HR 1.4; 95% CI 1.2-1.5; p<0.0001), but was not a significant predictor of SP (HR 0.91; 95% CI 0.7-1.2; p=0.46), CSS (HR 1.1; 95% CI 0.8-1.6; p=0.48), or OS (HR 1.1; 95% CI 0.92-1.2; p=0.37). CONCLUSIONS: The presence of a PSM increased the risks of BCR and LR following RP, but did not independently impact SP, CSS, or OS. These results should be considered when evaluating patients for
INTRODUCTION AND OBJECTIVE: A subset of men diagnosed with prostate cancer will have a greater risk of occult metastatic disease or recurrence following definitive therapy. Accurate identification of such patients is important for counseling, rational application of neoadjuvant/ adjuvant therapy, and the design and powering of clinical trials. Prediction tools generate estimates of clinical outcomes and can stratify patients by their predicted risk of disease recurrence. We compared the performance of 6 risk stratification models and the Kattan preoperative nomogram in predicting PSA relapse in high risk patients in order to determine the best tool for patient counseling. METHODS: We evaluated the performance of the 7 prediction tools in a population of men who underwent radical prostatectomy at the Cleveland Clinic from 1987 to 1995 (n=708) and 1996 to 2007 (n=3351). Freedom from biochemical relapse (FBR) at 5 years was our clinical endpoint. Performance of the 7 tools was evaluated based on discrimination (using c-index) and calibration. Trellis plots of nomogram-predicted 5-year FBR by each risk definition were performed to assess the degree of heterogeneity in the risk of PSA relapse within each high risk group. RESULTS: All 7 models discriminated better than random chance (Fig 1A). Multivariable models had superior discriminating ability with the Kattan nomogram showing the highest c-index. All 7 models demonstrated good calibration. There was significant heterogeneity within each of the 6 high risk groups with regards to predicted 5-year FBR rates when compared to their respective non-high risk counterparts (Fig 1B). CONCLUSIONS: These data confirm the value of using prediction tools to estimate outcomes in men with prostate cancer. However, patients are likely better served with individualized predictions that do not depend on group data. Despite incorporating the same preoperative variables, the Kattan nomogram was more accurate than any risk grouping in predicting PSA relapse in our patient cohort. Furthermore, given the considerable heterogeneity in the risk of PSA relapse in each of the 6 high risk definitions, risk stratification alone is not suitable for guiding treatment decisions for the individual patient.
Source of Funding: None