Clinical Utility of Melanoma Sentinel Lymph Node Biopsy Nomograms

Author:

Drebin Harrison M1,Hosein Sharif2,Kurtansky Nicholas R2,Nadelmann Emily2,Moy Andrea P3,Ariyan Charlotte E1,Bello Danielle M1,Brady Mary S1,Coit Daniel G1,Marchetti Michael A24,Bartlett Edmund K1

Affiliation:

1. From the Gastric and Mixed Tumor Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY (Drebin, Ariyan, Bello, Brady, Coit, Bartlett)

2. the Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY (Hosein, Kurtansky, Nadelmann, Marchetti)

3. the Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY (Moy)

4. Skagit Regional Health, Mt Vernon, WA (Marchetti).

Abstract

BACKGROUND: For patients with melanoma, the decision to perform sentinel lymph node biopsy (SLNB) is based on the estimated risk of lymph node metastasis. We assessed 3 melanoma SLNB risk-prediction models’ statistical performance and their ability to improve clinical decision making (clinical utility) on a cohort of melanoma SLNB cases. STUDY DESIGN: Melanoma patients undergoing SLNB at a single center from 2003 to 2021 were identified. The predicted probabilities of sentinel lymph node positivity using the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center (MSK), and Friedman nomograms were calculated. Receiver operating characteristic and calibration curves were generated. Clinical utility was assessed via decision curve analysis, calculating the net SLNBs that could have been avoided had a given model guided selection at different risk thresholds. RESULTS: Of 2,464 melanoma cases that underwent SLNB, 567 (23.0%) had a positive sentinel lymph node. The areas under the receiver operating characteristic curves for the Melanoma Institute of Australia, MSK, and Friedman models were 0.726 (95% CI, 0.702 to 0.750), 0.720 (95% CI, 0.697 to 0.744), and 0.721 (95% CI, 0.699 to 0.744), respectively. For all models, calibration was best at predicted positivity rates below 30%. The MSK model underpredicted risk. At a 10% risk threshold, only the Friedman model would correctly avoid a net of 6.2 SLNBs per 100 patients. The other models did not reduce net avoidable SLNBs at risk thresholds of ≤10%. CONCLUSIONS: The tested nomograms had comparable performance in our cohort. The only model that achieved clinical utility at risk thresholds of ≤10% was the Friedman model.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Surgery

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