title: Prediction of lymph node involvement in patients with breast tumors measuring 3-5 cm in a middle-income setting: the role of CancerMath.
abstract: BACKGROUND
  In settings with limited resources, sentinel lymph node biopsy (SNB) is only offered to breast cancer patients with small tumors and a low a priori risk of axillary metastases.
  
  
  OBJECTIVE
  We investigated whether CancerMath, a free online prediction tool for axillary lymph node involvement, is able to identify women at low risk of axillary lymph node metastases in Malaysian women with 3-5 cm tumors, with the aim to offer SNB in a targeted, cost-effective way.
  
  
  METHODS
  Women with non-metastatic breast cancers, measuring 3-5 cm were identified within the University Malaya Medical Centre (UMMC) breast cancer registry. We compared CancerMath-predicted probabilities of lymph node involvement between women with versus without lymph node metastases. The discriminative performance of CancerMath was tested using receiver operating characteristic (ROC) analysis.
  
  
  RESULTS
  Out of 1,017 patients, 520 (51 %) had axillary involvement. Tumors of women with axillary involvement were more often estrogen-receptor positive, progesterone-receptor positive, and human epidermal growth factor receptor (HER)-2 positive. The mean CancerMath score was higher in women with axillary involvement than in those without (53.5 vs. 51.3, p = 0.001). In terms of discrimination, CancerMath performed poorly, with an area under the ROC curve of 0.553 (95 % confidence interval CI 0.518-0.588). Attempts to optimize the CancerMath model by adding ethnicity and HER2 to the model did not improve discriminatory performance.
  
  
  CONCLUSIONS
  For Malaysian women with tumors measuring 3-5 cm, CancerMath is unable to accurately predict lymph node involvement and is therefore not helpful in the identification of women at low risk of node-positive disease who could benefit from SNB.
authors.affiliation: Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands, estherpijnappel@icloud.com.
authors.forename: E N
authors.initials: EN
authors.lastname: Pijnappel
authors.forename: N
authors.initials: N
authors.lastname: Bhoo-Pathy
authors.forename: J
authors.initials: J
authors.lastname: Suniza
authors.forename: M H
authors.initials: MH
authors.lastname: See
authors.forename: G H
authors.initials: GH
authors.lastname: Tan
authors.forename: C H
authors.initials: CH
authors.lastname: Yip
authors.forename: M
authors.initials: M
authors.lastname: Hartman
authors.forename: N A
authors.initials: NA
authors.lastname: Taib
authors.forename: H M
authors.initials: HM
authors.lastname: Verkooijen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001365:Axilla; D001943:Breast Neoplasms / Q000737:chemistry / Q000473:pathology*; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008296:Malaysia; D055641:Mathematical Concepts; D008875:Middle Aged; D011379:Prognosis; D012372:ROC Curve; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D021701:Sentinel Lymph Node Biopsy; D047368:Tumor Burden; D055815:Young Adult
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone