Annals of Surgical Oncology

, Volume 26, Issue 12, pp 3955–3961 | Cite as

Age and Lymphovascular Invasion Accurately Predict Sentinel Lymph Node Metastasis in T2 Melanoma Patients

  • Michael E. EggerEmail author
  • Megan Stevenson
  • Neal Bhutiani
  • Adrienne C. Jordan
  • Charles R. Scoggins
  • Prejesh Philips
  • Robert C. G. MartinII
  • Kelly M. McMasters



The risk of sentinel lymph node (SLN) metastasis in melanoma is related directly to tumor thickness and inversely to age. The authors hypothesized that for T2 (thickness 1.1–2.0 mm) melanoma, age, and other factors may be able to identify a cohort of patients with a low risk of SLN metastases.


The authors developed logistic regression models to predict positive SLNs in patients undergoing SLN biopsy for T2 melanoma using the National Cancer Database. Classification and regression-tree analysis were used to identify groups of patients with high and low risk for SLN metastases. The prediction model then was applied to a separate data set from a multicenter randomized clinical trial.


The study identified 12,918 patients with T2 melanoma undergoing SLN biopsy with clinically node-negative melanoma. In the multivariable analysis, increasing thickness, younger age, lymphovascular invasion (LVI), mitotic rate of 1/mm2 or more, axial location, and Clark level of 4 or 5 were independent risk factors for SLN metastases. A cohort based on age (> 56 years) and no LVI was identified with a relatively low risk (7.8%; 95% confidence interval 7.2–8.4%) of SLN metastases. The independent data set of 1531 patients with T2 melanoma confirmed these findings. Among elderly patients (age > 75 years) with melanoma 1.2 mm or smaller and no LVI, the risk of a positive SLN was 4.9% (95% confidence interval 3.3–7.1%).


Younger age and LVI are powerful predictors of SLN metastases for patients with T2 melanoma. This prediction model can inform shared decision-making regarding whether to perform SLN biopsy for older patients with otherwise low-risk T2 melanoma.



Kelly M. McMasters serves on the Scientific Advisory Board for Elucida Oncology. The National Cancer Data Base (NCDB) is a joint project of the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society. The CoC’s NCDB and the hospitals participating in the CoC NCDB are the source of the de-identified data used in this study. They have not verified the statistical validity of the data analysis or the conclusions derived by the authors and are not responsible for these.

Supplementary material

10434_2019_7690_MOESM1_ESM.pdf (56 kb)
Fig. S1 Flow diagram showing how a classification and regression tree (CART) model is built. The tree is built by the parsing of variables, one by one, to reduce the variation of the outcome in each successive split. This is the process of building or growing the tree. Including an excessive number of variables can lead to overfitting of the data and excessive complexity. The model then is “pruned” according to a cost-complexity algorithm, eliminating unnecessary variables and splits that do not significantly add to the ability of the model to discriminate the outcomes. The variables are chosen based on their ability to parse the data into meaningful subgroups. The analyst chooses only which variables to consider in the model. All variables are investigated, and only the statistically significant ones are retained, based on the number of final leaves (groups) requested by the analyst. (PDF 55 kb)


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Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  • Michael E. Egger
    • 1
    Email author
  • Megan Stevenson
    • 1
  • Neal Bhutiani
    • 1
  • Adrienne C. Jordan
    • 2
  • Charles R. Scoggins
    • 1
  • Prejesh Philips
    • 1
  • Robert C. G. MartinII
    • 1
  • Kelly M. McMasters
    • 1
  1. 1.The Hiram C. Polk Jr, MD Department of SurgeryUniversity of Louisville School of MedicineLouisvilleUSA
  2. 2.Department of Pathology and Laboratory MedicineUniversity of Louisville School of MedicineLouisvilleUSA

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