title: Predictive model of outcome of targeted nodal assessment in colorectal cancer.
abstract: BACKGROUND
  Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making.
  
  
  OBJECTIVE
  To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions.
  
  
  METHODS
  Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model.
  
  
  RESULTS
  Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively.
  
  
  CONCLUSIONS
  This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.
authors.affiliation: Department of Surgery, Division of Surgical Oncology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
authors.forename: Aviram
authors.initials: A
authors.lastname: Nissan
authors.forename: Mladjan
authors.initials: M
authors.lastname: Protic
authors.forename: Anton
authors.initials: A
authors.lastname: Bilchik
authors.forename: John
authors.initials: J
authors.lastname: Eberhardt
authors.forename: George E
authors.initials: GE
authors.lastname: Peoples
authors.forename: Alexander
authors.initials: A
authors.lastname: Stojadinovic
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D000368:Aged; D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000473:pathology*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D021701:Sentinel Lymph Node Biopsy / Q000706:statistics & numerical data*