Features predicting XXXX .

@entity5 @entity3852 @entity1 @entity1600
@entity3852 ( @entity3852 ) is a relatively rare, potentially aggressive cutaneous @entity5 . We examined the clinical and histologic features of primary @entity3852 that may correlate with the probability of a positive @entity1600 ( @entity1600 ). METHODS: Ninety-five @entity1 with @entity3852 who underwent @entity1600 biopsy at the University of Michigan were identified. @entity1600 biopsy was performed on 97 @entity5 , and an @entity1600 was identified in 93 instances. These were reviewed for clinical and histologic features and associated @entity1600 positivity. Univariate associations between these characteristics and a positive @entity1600 were tested for by using either the   (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 @entity1600 . RESULTS: @entity1600 positivity was significantly associated with the clinical size of the lesion, greatest horizontal histologic dimension, @entity5 thickness, mitotic rate, and histologic growth pattern. Two competing multivariate models were generated to predict a positive @entity1600 . The histologic growth pattern was present in both models and combined with either @entity5 thickness or mitotic rate. CONCLUSION: Increasing clinical size, increasing @entity5 thickness, increasing mitotic rate, and infiltrative @entity5 growth pattern were significantly associated with a greater likelihood of a positive @entity1600 . By using the growth pattern and @entity5 thickness model, no subgroup of @entity1 was predicted to have a lower than 15% to 20% likelihood of a positive @entity1600 . This suggests that all @entity1 presenting with @entity3852 without clinical evidence of regional @entity1600 should be considered for @entity1600 biopsy.
