title: A nomogram to predict for malignant diagnosis of BI-RADS Category 4 breast lesions.
abstract: OBJECTIVE
  BI-RADS Category classification is the most powerful predictor of breast cancer (BC). However, BI-RADS Category 4 lesions are associated with a highly variable rate of BC. The purpose of this study was to develop and validate a nomogram for the prediction of individual probability of BC in patients with BI-RADS Category 4 lesions.
  
  
  METHODS
  The study included all patients with BI-RADS Category 4 lesions at screening mammogram, who underwent diagnostic cytology or biopsy and, as needed, surgery or follow-up. Univariate and multivariate logistic regression analyses were used to develop the model and build the nomogram. This nomogram was evaluated on a training set of 170 patients treated at IGR Cancer Center, Paris, France. Nomogram performance was evaluated on an external independent dataset of 188 patients from MDA Cancer Center, Houston, Texas.
  
  
  RESULTS
  A total of 51 (28.5%) patients in the training set and 73 (42.4%) patients in the validation set were diagnosed with BC. The final, most informative, nomogram included information on patient age (P = 0.04), palpable tumor (P = 0.002), menopausal status (P = 0.32), lesion size (P = 0.81), HRT (P = 0.09), and Gail risk (P = 0.58). The predictive accuracy of the nomogram was 0.716, respectively. The concordance index of the model was 0.66 in the validation set.
  
  
  CONCLUSIONS
  The nomogram based on clinical and radiological findings may help inform the patients before surgical explorations, to decrease the number of missed cancer cases but currently cannot replace FNA or biopsy.
authors.affiliation: Department of Breast Oncology, Institut Gustave Roussy, Villejuif, France.
authors.forename: Chafika
authors.initials: C
authors.lastname: Mazouni
authors.forename: Nour
authors.initials: N
authors.lastname: Sneige
authors.forename: Roman
authors.initials: R
authors.lastname: Rouzier
authors.forename: Corinne
authors.initials: C
authors.lastname: Balleyguier
authors.forename: Therese
authors.initials: T
authors.lastname: Bevers
authors.forename: Fabrice
authors.initials: F
authors.lastname: André
authors.forename: Philippe
authors.initials: P
authors.lastname: Vielh
authors.forename: Suzette
authors.initials: S
authors.lastname: Delaloge
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001706:Biopsy; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003657:Decision Making; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D008875:Middle Aged; D049451:Nomograms; D011336:Probability; D012189:Retrospective Studies