Performance of models predicting residual lymph node disease in melanoma patients following sentinel lymph node biopsy.

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.