Giorgio Gandaglia1, Nicola Fossati1, Emanuele Zaffuto2, Marco Bandini1, Paolo Dell'Oglio1, Carlo Andrea Bravi1, Giuseppe Fallara1, Francesco Pellegrino1, Luigi Nocera1, Pierre I Karakiewicz3, Zhe Tian3, Massimo Freschi4, Rodolfo Montironi5, Francesco Montorsi1, Alberto Briganti6. 1. Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy. 2. Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Canada. 3. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Canada. 4. Unità Operativa Anatomia Patologica, IRCCS Ospedale San Raffaele, Milan, Italy. 5. Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy. 6. Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy. Electronic address: briganti.alberto@hsr.it.
Abstract
BACKGROUND: Preoperative assessment of the risk of lymph node invasion (LNI) is mandatory to identify prostate cancer (PCa) patients who should receive an extended pelvic lymph node dissection (ePLND). OBJECTIVE: To update a nomogram predicting LNI in contemporary PCa patients with detailed biopsy reports. DESIGN, SETTING, AND PARTICIPANTS: Overall, 681 patients with detailed biopsy information, evaluated by a high-volume uropathologist, treated with radical prostatectomy and ePLND between 2011 and 2016 were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A multivariable logistic regression model predicting LNI was fitted and represented the basis for a coefficient-based nomogram. The model was evaluated using the receiver operating characteristic-derived area under the curve (AUC), calibration plot, and decision-curve analyses (DCAs). RESULTS AND LIMITATIONS: The median number of nodes removed was 16. Overall, 79 (12%) patients had LNI. A multivariable model that included prostate-specific antigen, clinical stage, biopsy Gleason grade group, percentage of cores with highest-grade PCa, and percentage of cores with lower-grade disease represented the basis for the nomogram. After cross validation, the predictive accuracy of these predictors in our cohort was 90.8% and the DCA demonstrated improved risk prediction against threshold probabilities of LNI ≤20%. Using a cutoff of 7%, 471 (69%) ePLNDs would be spared and LNI would be missed in seven (1.5%) patients. As compared with the Briganti and Memorial Sloan Kettering Cancer Center nomograms, the novel model showed higher AUC (90.8% vs 89.5% vs 89.5%), better calibration characteristics, and a higher net benefit at DCA. CONCLUSIONS: An ePLND should be avoided in patients with detailed biopsy information and a risk of nodal involvement below 7%, in order to spare approximately 70% ePLNDs at the cost of missing only 1.5% LNIs. PATIENT SUMMARY: We developed a novel nomogram to predict lymph node invasion (LNI) in patients with clinically localized prostate cancer based on detailed biopsy reports. A lymph node dissection exclusively in men with a risk of LNI >7% according to this model would significantly reduce the number of unnecessary pelvic nodal dissections with a risk of missing only 1.5% of patients with LNI.
BACKGROUND: Preoperative assessment of the risk of lymph node invasion (LNI) is mandatory to identify prostate cancer (PCa) patients who should receive an extended pelvic lymph node dissection (ePLND). OBJECTIVE: To update a nomogram predicting LNI in contemporary PCa patients with detailed biopsy reports. DESIGN, SETTING, AND PARTICIPANTS: Overall, 681 patients with detailed biopsy information, evaluated by a high-volume uropathologist, treated with radical prostatectomy and ePLND between 2011 and 2016 were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A multivariable logistic regression model predicting LNI was fitted and represented the basis for a coefficient-based nomogram. The model was evaluated using the receiver operating characteristic-derived area under the curve (AUC), calibration plot, and decision-curve analyses (DCAs). RESULTS AND LIMITATIONS: The median number of nodes removed was 16. Overall, 79 (12%) patients had LNI. A multivariable model that included prostate-specific antigen, clinical stage, biopsy Gleason grade group, percentage of cores with highest-grade PCa, and percentage of cores with lower-grade disease represented the basis for the nomogram. After cross validation, the predictive accuracy of these predictors in our cohort was 90.8% and the DCA demonstrated improved risk prediction against threshold probabilities of LNI ≤20%. Using a cutoff of 7%, 471 (69%) ePLNDs would be spared and LNI would be missed in seven (1.5%) patients. As compared with the Briganti and Memorial Sloan Kettering Cancer Center nomograms, the novel model showed higher AUC (90.8% vs 89.5% vs 89.5%), better calibration characteristics, and a higher net benefit at DCA. CONCLUSIONS: An ePLND should be avoided in patients with detailed biopsy information and a risk of nodal involvement below 7%, in order to spare approximately 70% ePLNDs at the cost of missing only 1.5% LNIs. PATIENT SUMMARY: We developed a novel nomogram to predict lymph node invasion (LNI) in patients with clinically localized prostate cancer based on detailed biopsy reports. A lymph node dissection exclusively in men with a risk of LNI >7% according to this model would significantly reduce the number of unnecessary pelvic nodal dissections with a risk of missing only 1.5% of patients with LNI.
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