Use and Costs of Sentinel Lymph Node Biopsy in Non-Ulcerated T1b Melanoma: Analysis of a Population-Based Registry

Association of sociodemographic characteristics with utilization of sentinel lymph node biopsy for American Joint Committee on Cancer 8th edition T1b cutaneous melanoma

Sentinel lymph node biopsy (SLNB) is an important staging and prognostic tool for cutaneous melanoma (CM). However, there exists a knowledge gap regarding whether sociodemographic characteristics are associated with receipt of SLNB for T1b CMs, for which there are no definitive recommendations for SLNB per current National Comprehensive Cancer Network guidelines. We performed a retrospective analysis of the 2012-2018 National Cancer Database, identifying patients with American Joint Committee on Cancer staging manual 8th edition stage T1b CM, and used multivariable logistic regression to analyze associations between sociodemographic characteristics and receipt of SLNB. Among 40,458 patients with T1b CM, 23,813 (58.9%) received SLNB. Median age was 62 years, and most patients were male (57%) and non-Hispanic White (95%). In multivariable analyses, patients of Hispanic (aOR 0.67, 95%CI 0.48-0.94) and other (aOR 0.78, 95%CI 0.63-0.97) race/ethnicity, and patients aged > 75 (aOR 0.33, 95%CI 0.29-0.38), were less likely to receive SLNB. Conversely, patients in the highest of seven socioeconomic status levels (aOR 1.37, 95%CI 1.13-1.65) and those treated at higher-volume facilities (aOR 1.29, 95%CI 1.14-1.46) were more likely to receive SLNB. Understanding the underlying drivers of these associations may yield important insights for the management of patients with melanoma.

External validation of the Melanoma Institute Australia Sentinel Node Metastasis Risk Prediction Tool using the National Cancer Database

BACKGROUND

To improve patient selection for sentinel node (SN) biopsy, the Melanoma Institute of Australia (MIA) created a predictive model based on readily available clinicopathologic factors.

OBJECTIVES

Validation of the MIA nomogram using the National Cancer Database (NCDB), a nationwide oncology outcomes database for >1500 Commission-accredited cancer programs in the United States.

METHODS

A total of 60,165 patients were included in the validation. The probability of SN positivity was calculated for each patient. Using calculated probabilities, a receiver operating characteristic curve was generated to assess the model's discrimination ability.

RESULTS

At baseline, the NCDB cohort had different clinicopathologic characteristics compared with the original MIA data set. Despite these differences, the MIA nomogram retained high-predictive accuracy within the NCDB dataset (C-statistic, 0.733 [95% CI, 0.726-0.739]), although calibration weakened for the highest risk decile.

LIMITATIONS

The NCDB collects data from hospital registries accredited by the Commission on Cancer.

CONCLUSIONS

In conclusion, this study validated the use of the MIA nomogram in a nationwide oncology outcomes database collected from >1500 Commission-accredited cancer programs in the United States, demonstrating the potential for this nomogram to predict SN positivity and reduce the number of negative SN biopsies.