title: Sentinel node status prediction by four statistical models: results from a large bi-institutional series (n = 1132).
abstract: OBJECTIVE
  To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status.
  
  
  BACKGROUND
  About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNBassociated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status.
  
  
  METHODS
  The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate.
  
  
  RESULTS
  After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively).
  
  
  CONCLUSIONS
  Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients ( approximately 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.
authors: Department of Oncological and Surgical Sciences, University of Padova, Padova, Italy. simone.mocellin@unipd.it
authors: Simone
authors: S
authors: Mocellin
authors: John F
authors: JF
authors: Thompson
authors: Sandro
authors: S
authors: Pasquali
authors: Maria C
authors: MC
authors: Montesco
authors: Pierluigi
authors: P
authors: Pilati
authors: Donato
authors: D
authors: Nitti
authors: Robyn P
authors: RP
authors: Saw
authors: Richard A
authors: RA
authors: Scolyer
authors: Jonathan R
authors: JR
authors: Stretch
authors: Carlo R
authors: CR
authors: Rossi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008545:Melanoma / Q000175:diagnosis / Q000556:secondary*; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D021701:Sentinel Lymph Node Biopsy / Q000706:statistics & numerical data*; D012878:Skin Neoplasms / Q000473:pathology*; D055815:Young Adult