Objective: To validate a nomogram predicting lymph node invasion (LNI) in prostate cancer patients undergoing radical prostatectomy taking into consideration multiparametric-magnetic resonance imaging (mp-MRI) parameters and targeted biopsies in a western European cohort. Patients and Methods: A total of 473 men diagnosed by targeted biopsies, using software-based MRI-ultrasound image fusion system, and operated by radical prostatectomy with extended pelvic lymph node dissection across 11 Europeans centers between 2012 and 2019 were identified. Area under the curve of the receiver operator characteristic curve, calibration plot and decision curve analysis were used to evaluated the performance of the model. Results: Overall, 56 (11.8%) patients had LNI on final pathologic examination with a median (IQR) of 13 (9–18) resected nodes. Significant differences (all P < 0.05) were found between patients with and without LNI in terms of preoperative PSA, clinical stage at DRE and mp-MRI, maximum diameter of the index lesion, PI-RADS score, Grade Group on systematic and targeted biopsies, total number of dissected lymph nodes, final pathologic staging and Grade Group. External validation of the prediction model showed a good accuracy with an area under the curve calculated as 0.8 (CI 95% 0.75–0.86). Graphic analysis of calibration plot and decision curve analysis showed a slight underestimation for predictive probability for LNI between 3% and 22% and a high net benefit. A cut-off at 7% was associated with a risk of missing LNI in 2.6%, avoiding unnecessary surgeries in 55.9%. Conclusions: We report an external validation of the nomogram predicting LNI in patients treated with extended pelvic lymph node dissection in a western European cohort and a cut-off at 7% seems appropriate.

External validation of the Briganti nomogram predicting lymph node invasion in patients with intermediate and high-risk prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies: A European multicenter study

Oderda M.;Simone G.;Gontero P.;
2020-01-01

Abstract

Objective: To validate a nomogram predicting lymph node invasion (LNI) in prostate cancer patients undergoing radical prostatectomy taking into consideration multiparametric-magnetic resonance imaging (mp-MRI) parameters and targeted biopsies in a western European cohort. Patients and Methods: A total of 473 men diagnosed by targeted biopsies, using software-based MRI-ultrasound image fusion system, and operated by radical prostatectomy with extended pelvic lymph node dissection across 11 Europeans centers between 2012 and 2019 were identified. Area under the curve of the receiver operator characteristic curve, calibration plot and decision curve analysis were used to evaluated the performance of the model. Results: Overall, 56 (11.8%) patients had LNI on final pathologic examination with a median (IQR) of 13 (9–18) resected nodes. Significant differences (all P < 0.05) were found between patients with and without LNI in terms of preoperative PSA, clinical stage at DRE and mp-MRI, maximum diameter of the index lesion, PI-RADS score, Grade Group on systematic and targeted biopsies, total number of dissected lymph nodes, final pathologic staging and Grade Group. External validation of the prediction model showed a good accuracy with an area under the curve calculated as 0.8 (CI 95% 0.75–0.86). Graphic analysis of calibration plot and decision curve analysis showed a slight underestimation for predictive probability for LNI between 3% and 22% and a high net benefit. A cut-off at 7% was associated with a risk of missing LNI in 2.6%, avoiding unnecessary surgeries in 55.9%. Conclusions: We report an external validation of the nomogram predicting LNI in patients treated with extended pelvic lymph node dissection in a western European cohort and a cut-off at 7% seems appropriate.
2020
38
11
1
8
Lymph node invasion; MRI; Nomogram; Prostate cancer; Targeted biopsy
Diamand R.; Oderda M.; Albisinni S.; Fourcade A.; Fournier G.; Benamran D.; Iselin C.; Fiard G.; Descotes J.-L.; Assenmacher G.; Svistakov I.; Peltier A.; Simone G.; Di Cosmo G.; Roche J.-B.; Bonnal J.-L.; Van Damme J.; Rossi M.; Mandron E.; Gontero P.; Roumeguere T.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1781836
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