title: Models predicting non-sentinel node involvement also predict for regional recurrence in breast cancer patients without axillary treatment.
abstract: BACKGROUND
  Non-SN prediction models are frequently used in clinical decision making to identify patients that may not need axillary treatment, but these models still need to be validated by follow-up data. Our purpose was the validation of non-sentinel node (SN) prediction models in predicting regional recurrences in patients without axillary treatment.
  
  
  METHODS
  We followed a cohort of 486 women with favorable primary tumor characteristics and pN0(i+)(sn) or pN1mi(sn) for median 4.5 years. None of the patients underwent axillary treatment. Based on four published non-SN prediction models, the threshold allowing separation into low versus high-risk on non-SN involvement was set at 10%.
  
  
  RESULTS
  Overall 5-year regional recurrence rate was 3.0% (SE, ±0.1%). Using the Tenon scoring system, 438 low-risk patients had a 5-year regional recurrence rate of 2.3% (±0.8%), and 48 high-risk patients a recurrence rate of 10.1% (±0.4%). The MSKCC nomogram identified 300 low-risk patients with a recurrence rate of 2.8% (±1.1%), versus 166 high-risk patients with a rate of 3.4% (±0.5%) (20 patients not assessable). The Stanford nomogram identified 21 high-risk patients without recurrence, and 465 low-risk patients with a 3.2% (±0.9%) recurrence rate. A Dutch model discriminated between 384 low-risk patients with a recurrence rate of 2.2% (±0.8%) and 102 high-risk patients with a rate of 6.3% (±2.9%).
  
  
  CONCLUSIONS
  The Tenon scoring system outperformed the other models as it identified the largest subgroup of patients with low recurrence rate. In patients resembling our cohort we would recommend axillary treatment if they had a Tenon score above 3.5.
authors.affiliation: Department of Medical Oncology, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: M J A E
authors.initials: MJ
authors.lastname: Pepels
authors.forename: J H M J
authors.initials: JH
authors.lastname: Vestjens
authors.forename: M
authors.initials: M
authors.lastname: de Boer
authors.forename: P
authors.initials: P
authors.lastname: Bult
authors.forename: J A A M
authors.initials: JA
authors.lastname: Van Dijck
authors.forename: M
authors.initials: M
authors.lastname: Menke-Pluijmers
authors.forename: P J
authors.initials: PJ
authors.lastname: van Diest
authors.forename: G
authors.initials: G
authors.lastname: Borm
authors.forename: V C G
authors.initials: VC
authors.lastname: Tjan-Heijnen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001365:Axilla / Q000473:pathology; D001943:Breast Neoplasms / Q000473:pathology*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008875:Middle Aged; D061206:Neoplasm Micrometastasis / Q000473:pathology; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D009367:Neoplasm Staging; D049451:Nomograms; D018570:Risk Assessment / Q000379:methods
keywords: Breast cancer; Nomograms; Regional recurrence; Sentinel node