title: Features predicting sentinel lymph node positivity in Merkel cell carcinoma.
abstract: OBJECTIVE
  Merkel cell carcinoma (MCC) is a relatively rare, potentially aggressive cutaneous malignancy. We examined the clinical and histologic features of primary MCC that may correlate with the probability of a positive sentinel lymph node (SLN).
  
  
  METHODS
  Ninety-five patients with MCC who underwent SLN biopsy at the University of Michigan were identified. SLN biopsy was performed on 97 primary tumors, and an SLN was identified in 93 instances. These were reviewed for clinical and histologic features and associated SLN positivity. Univariate associations between these characteristics and a positive SLN were tested for by using either the χ(2) or the Fisher's exact test. A backward elimination algorithm was used to help create a best multiple variable model to explain a positive SLN.
  
  
  RESULTS
  SLN positivity was significantly associated with the clinical size of the lesion, greatest horizontal histologic dimension, tumor thickness, mitotic rate, and histologic growth pattern. Two competing multivariate models were generated to predict a positive SLN. The histologic growth pattern was present in both models and combined with either tumor thickness or mitotic rate.
  
  
  CONCLUSIONS
  Increasing clinical size, increasing tumor thickness, increasing mitotic rate, and infiltrative tumor growth pattern were significantly associated with a greater likelihood of a positive SLN. By using the growth pattern and tumor thickness model, no subgroup of patients was predicted to have a lower than 15% to 20% likelihood of a positive SLN. This suggests that all patients presenting with MCC without clinical evidence of regional lymph node disease should be considered for SLN biopsy.
authors.affiliation: University of Michigan Health System, 1910 Taubman Center, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0314, USA. jennschw@med.umich.edu
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Schwartz
authors.forename: Kent A
authors.initials: KA
authors.lastname: Griffith
authors.forename: Lori
authors.initials: L
authors.lastname: Lowe
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Wong
authors.forename: Scott A
authors.initials: SA
authors.lastname: McLean
authors.forename: Douglas R
authors.initials: DR
authors.lastname: Fullen
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Lao
authors.forename: James A
authors.initials: JA
authors.lastname: Hayman
authors.forename: Carol R
authors.initials: CR
authors.lastname: Bradford
authors.forename: Riley S
authors.initials: RS
authors.lastname: Rees
authors.forename: Timothy M
authors.initials: TM
authors.lastname: Johnson
authors.forename: Christopher K
authors.initials: CK
authors.lastname: Bichakjian
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015266:Carcinoma, Merkel Cell / Q000473:pathology*; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008297:Male; D008824:Michigan; D008875:Middle Aged; D008940:Mitotic Index; D009361:Neoplasm Invasiveness; D016017:Odds Ratio; D018579:Patient Selection; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D021701:Sentinel Lymph Node Biopsy; D012878:Skin Neoplasms / Q000473:pathology*