Sandra MacDonald1, Jodi Siever2, Christopher Baliski3. 1. BC Cancer-Sindi Ahluwalia Hawkins Centre, Dept. of Surgical Oncology, 399 Royal Ave, Kelowna, BC, V1Y 5L3, Canada; University of British Columbia Southern Medical Program, 2312 Pandosy Street, Kelowna, BC, V1Y 1T3, Canada. Electronic address: Sandra.macdonald@alumni.ubc.ca. 2. University of British Columbia Southern Medical Program, 2312 Pandosy Street, Kelowna, BC, V1Y 1T3, Canada. Electronic address: Jodi.siever@ubc.ca. 3. BC Cancer-Sindi Ahluwalia Hawkins Centre, Dept. of Surgical Oncology, 399 Royal Ave, Kelowna, BC, V1Y 5L3, Canada. Electronic address: CBaliski@bccancer.bc.ca.
Abstract
BACKGROUND: Among melanoma patients with a tumor-positive sentinel node biopsy (SNB), approximately 20% harbor disease in non-sentinel nodes (nSN), as determined by a completion lymph node dissection (CLND). CLND lacks a survival benefit and has high morbidity. This study assesses predictive factors for nSN metastasis and validates five models predicting nSN metastasis. METHODS: Patients with invasive melanoma were identified from the BC Cancer Agency (2005-2015). Clinicopathological data were collected from 296 patients who underwent a CLND after a positive SNB. Multivariate analysis was completed to assess predictive variables in the study population. Five models were externally validated using overall model performance (Brier score [calibration and discrimination]) and discrimination (area under the ROC curve [AUC]). RESULTS: Seventy-three patients had nSN metastasis at the time of CLND. The variable most predictive of nSN involvement was lymphovascular invasion (odds ratio [OR] 3.99; 95% confidence interval [CI] 1.67-9.54; p = 0.002). The highest discrimination was Lee et al. (2004) (AUC 0.68 [95% CI 0.61-0.75]), Rossi et al. (2018) (AUC 0.68 [95% CI 0.57-0.77]), and Bertolli et al. (2019) (AUC 0.68 [95% CI 0.60-0.75]). Rossi et al. (2018) had the lowest overall model performance (Brier score 0.44). Rossi et al. (2018) and Bertolli et al. (2019) had the ability to stratify patients to a risk of nSN involvement up to 99% and 95%, respectively. CONCLUSION: Bertolli et al. (2019) had amongst the highest overall model performance, was the most clinically meaningful and is recommended as the preferred model for predicting nSN metastasis.
BACKGROUND: Among melanomapatients with a tumor-positive sentinel node biopsy (SNB), approximately 20% harbor disease in non-sentinel nodes (nSN), as determined by a completion lymph node dissection (CLND). CLND lacks a survival benefit and has high morbidity. This study assesses predictive factors for nSN metastasis and validates five models predicting nSN metastasis. METHODS:Patients with invasive melanoma were identified from the BC Cancer Agency (2005-2015). Clinicopathological data were collected from 296 patients who underwent a CLND after a positive SNB. Multivariate analysis was completed to assess predictive variables in the study population. Five models were externally validated using overall model performance (Brier score [calibration and discrimination]) and discrimination (area under the ROC curve [AUC]). RESULTS: Seventy-three patients had nSN metastasis at the time of CLND. The variable most predictive of nSN involvement was lymphovascular invasion (odds ratio [OR] 3.99; 95% confidence interval [CI] 1.67-9.54; p = 0.002). The highest discrimination was Lee et al. (2004) (AUC 0.68 [95% CI 0.61-0.75]), Rossi et al. (2018) (AUC 0.68 [95% CI 0.57-0.77]), and Bertolli et al. (2019) (AUC 0.68 [95% CI 0.60-0.75]). Rossi et al. (2018) had the lowest overall model performance (Brier score 0.44). Rossi et al. (2018) and Bertolli et al. (2019) had the ability to stratify patients to a risk of nSN involvement up to 99% and 95%, respectively. CONCLUSION: Bertolli et al. (2019) had amongst the highest overall model performance, was the most clinically meaningful and is recommended as the preferred model for predicting nSN metastasis.
Authors: Kristy K Broman; Tasha M Hughes; Lesly A Dossett; James Sun; Michael J Carr; Dennis A Kirichenko; Avinash Sharma; Edmund K Bartlett; Amanda Ag Nijhuis; John F Thompson; Tina J Hieken; Lisa Kottschade; Jennifer Downs; David E Gyorki; Emma Stahlie; Alexander van Akkooi; David W Ollila; Jill Frank; Yun Song; Giorgos Karakousis; Marc Moncrieff; Jenny Nobes; John Vetto; Dale Han; Jeffrey Farma; Jeremiah L Deneve; Martin D Fleming; Matthew Perez; Kirsten Baecher; Michael Lowe; Roger Olofsson Bagge; Jan Mattsson; Ann Y Lee; Russell S Berman; Harvey Chai; Hidde M Kroon; Roland M Teras; Juri Teras; Norma E Farrow; Georgia M Beasley; Jane Yc Hui; Lukas Been; Schelto Kruijff; David Boulware; Amod A Sarnaik; Vernon K Sondak; Jonathan S Zager Journal: J Am Coll Surg Date: 2020-12-13 Impact factor: 6.532