Identification of the Optimal Candidates for Nodal Staging with Extended Pelvic Lymph Node Dissection Among Prostate Cancer Patients Who Underwent Preoperative Prostate-specific Membrane Antigen Positron Emission Tomography. External Validation of the Memorial Sloan Kettering Cancer Center and Briganti Nomograms and Development of a Novel Tool.
Lymph node invasion
Magnetic resonance imaging–targeted biopsy
Nomogram
Pelvic lymph node dissection
Prostate cancer
Prostate-specific membrane antigen positron emission tomography
Radical prostatectomy
Journal
European urology oncology
ISSN: 2588-9311
Titre abrégé: Eur Urol Oncol
Pays: Netherlands
ID NLM: 101724904
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
14
02
2023
revised:
15
04
2023
accepted:
06
05
2023
medline:
4
12
2023
pubmed:
4
6
2023
entrez:
3
6
2023
Statut:
ppublish
Résumé
Although the therapeutic role of extended pelvic lymph node dissection (ePLND) in patients with prostate cancer (PCa) is still under debate, this procedure is recommended for staging purposes in selected cases. Nomograms for predicting lymph node invasion (LNI) do not account for prostate-specific membrane antigen (PSMA) positron emission tomography (PET) imaging, which is characterized by a high negative predictive value for nodal metastases. To externally validate models predicting LNI in patients with miN0M0 PCa at PSMA PET and to develop a novel tool in this setting. Overall, 458 patients with miN0M0 disease undergoing radical prostatectomy (RP) and ePLND at 12 centers between 2017 and 2022 were identified. Available tools were externally validated using calibration plots, the area under the receiver operating characteristic curve (AUC), and decision curve analyses to assess calibration, discrimination, and the net benefit. A novel coefficient-based model was developed, internally validated, and compared with available tools. Overall, 53 patients (12%) had LNI. The AUC was 69% for the Briganti 2012, 64% for the Briganti 2017, 73% for the Briganti 2019, and 66% for the Memorial Sloan Kettering Cancer Center nomogram. Multiparametric magnetic resonance imaging stage, biopsy grade group 5, the diameter of the index lesion, and the percentage of positive cores at systematic biopsy were independent predictors of LNI (all p ≤ 0.04). Internal cross-validation confirmed a coefficient-based model with AUC of 78%, better calibration, and a higher net benefit in comparison to the other nomograms assessed. Use of a 5% cutoff would have spared 47% ePLND procedures (vs 13% for the Briganti 2019 nomogram) at the cost of missing only 2.1% LNI cases . The lack of central review of imaging and pathology represents the main limitation. Tools for predicting LNI are associated with suboptimal performance for men with miN0M0 PCa. We propose a novel model for predicting LNI that outperforms available tools in this population. Tools currently used to predict lymph node invasion (LNI) in prostate cancer are not optimal for men with negative node findings on PET (positron emission tomography) scans, leading to a high number of unnecessary extended pelvic lymph node dissection (ePLND) procedures. A novel tool should be used in clinical practice to identify candidates for ePLND to reduce the risk of unnecessary procedures without missing LNI cases.
Sections du résumé
BACKGROUND
BACKGROUND
Although the therapeutic role of extended pelvic lymph node dissection (ePLND) in patients with prostate cancer (PCa) is still under debate, this procedure is recommended for staging purposes in selected cases. Nomograms for predicting lymph node invasion (LNI) do not account for prostate-specific membrane antigen (PSMA) positron emission tomography (PET) imaging, which is characterized by a high negative predictive value for nodal metastases.
OBJECTIVE
OBJECTIVE
To externally validate models predicting LNI in patients with miN0M0 PCa at PSMA PET and to develop a novel tool in this setting.
DESIGN, SETTING, AND PARTICIPANTS
METHODS
Overall, 458 patients with miN0M0 disease undergoing radical prostatectomy (RP) and ePLND at 12 centers between 2017 and 2022 were identified.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES
METHODS
Available tools were externally validated using calibration plots, the area under the receiver operating characteristic curve (AUC), and decision curve analyses to assess calibration, discrimination, and the net benefit. A novel coefficient-based model was developed, internally validated, and compared with available tools.
RESULTS AND LIMITATIONS
CONCLUSIONS
Overall, 53 patients (12%) had LNI. The AUC was 69% for the Briganti 2012, 64% for the Briganti 2017, 73% for the Briganti 2019, and 66% for the Memorial Sloan Kettering Cancer Center nomogram. Multiparametric magnetic resonance imaging stage, biopsy grade group 5, the diameter of the index lesion, and the percentage of positive cores at systematic biopsy were independent predictors of LNI (all p ≤ 0.04). Internal cross-validation confirmed a coefficient-based model with AUC of 78%, better calibration, and a higher net benefit in comparison to the other nomograms assessed. Use of a 5% cutoff would have spared 47% ePLND procedures (vs 13% for the Briganti 2019 nomogram) at the cost of missing only 2.1% LNI cases . The lack of central review of imaging and pathology represents the main limitation.
CONCLUSIONS
CONCLUSIONS
Tools for predicting LNI are associated with suboptimal performance for men with miN0M0 PCa. We propose a novel model for predicting LNI that outperforms available tools in this population.
PATIENT SUMMARY
RESULTS
Tools currently used to predict lymph node invasion (LNI) in prostate cancer are not optimal for men with negative node findings on PET (positron emission tomography) scans, leading to a high number of unnecessary extended pelvic lymph node dissection (ePLND) procedures. A novel tool should be used in clinical practice to identify candidates for ePLND to reduce the risk of unnecessary procedures without missing LNI cases.
Identifiants
pubmed: 37270378
pii: S2588-9311(23)00086-X
doi: 10.1016/j.euo.2023.05.003
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
543-552Informations de copyright
Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.