title: Predicting patients at low probability of requiring postmastectomy radiation therapy.
abstract: BACKGROUND
  Postmastectomy radiation therapy (PMRT) is recommended for patients with four or more positive lymph nodes (LN+). Given the ramifications of PMRT for immediate reconstruction, we sought to create a model using preoperative and intraoperative factors to predict which patients with a positive sentinel lymph node will have less than four LN+.
  
  
  METHODS
  The database from a prospective multicenter study of 4,131 patients was used for this analysis. Patients with one to three positive sentinel lymph nodes (SLN) and tumors < 5 cm (n = 1,133) in size were randomly divided into a training set (n = 580) and a test set (n = 553). Multivariate logistic regression was used on the training set to create a prediction rule that was subsequently validated in the test set.
  
  
  RESULTS
  Median patient age was 57 (range, 27-100) years, and median tumor size was 2.0 (range, 0.2-4.8) cm. In the training set, factors associated with having four or more LN+ on multivariate analysis were: tumor size [odds ratio (OR) = 2.087; 95% confidence interval (CI): 1.307-3.333, P = 0.002), number of positive SLN (P < 0.0005), and proportion of positive SLN (OR = 3.602; 95% CI: 2.100-6.179, P < 0.005). A predictive model was established with a point assigned to each positive SLN, T2 (vs. T1), and if proportion of positive SLN was > 50%, for a maximum of five points. In both the training and test sets, patients with one point had a low probability of having four or more LN+ (3.8% and 3.3%, respectively).
  
  
  CONCLUSIONS
  Tumor size, number of positive SLN, and the proportion of positive SLN influence whether patients will have four or more LN+. A simple model can predict the probability of requiring PMRT.
authors.affiliation: Division of Surgical Oncology, University of Louisville, 315 East Broadway, Suite 312, Louisville, KY 40202, USA. Anees.chagpar@nortonhealthcare.org
authors.forename: Anees B
authors.initials: AB
authors.lastname: Chagpar
authors.forename: Charles R
authors.initials: CR
authors.lastname: Scoggins
authors.forename: Robert C G
authors.initials: RC
authors.lastname: Martin
authors.forename: Earl F
authors.initials: EF
authors.lastname: Cook
authors.forename: Terry
authors.initials: T
authors.lastname: McCurry
authors.forename: Nana
authors.initials: N
authors.lastname: Mizuguchi
authors.forename: Kristie J
authors.initials: KJ
authors.lastname: Paris
authors.forename: David J
authors.initials: DJ
authors.lastname: Carlson
authors.forename: Alison L
authors.initials: AL
authors.lastname: Laidley
authors.forename: Souzan E
authors.initials: SE
authors.lastname: El-Eid
authors.forename: Terre Q
authors.initials: TQ
authors.lastname: McGlothin
authors.forename: Kelly M
authors.initials: KM
authors.lastname: McMasters
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000532:radiotherapy* / Q000601:surgery; D019992:Databases as Topic; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology; D008207:Lymphatic Metastasis; D008408:Mastectomy; D008875:Middle Aged; D008954:Models, Biological; D011184:Postoperative Period; D011237:Predictive Value of Tests; D011336:Probability; D018714:Radiotherapy, Adjuvant; D011897:Random Allocation; D021701:Sentinel Lymph Node Biopsy