title: Predictive factors for malignancy in incidental pulmonary nodules detected in breast cancer patients at baseline CT.
abstract: OBJECTIVE
  Pulmonary nodules are commonly encountered at staging CTs in patients with extrathoracic malignancies, but their significance on a per-patient basis remains uncertain.
  
  
  METHODS
  We undertook a retrospective analysis of pulmonary nodules identified in patients with a diagnosis of breast cancer from 2010 - 2015, evaluating nodules present at a baseline CT (i.e. prevalent nodules). We reviewed 211 patients with 248 individual nodules.
  
  
  RESULTS
  The rate of malignancy in prevalent nodules is low, approximately 13 %. Variables associated with metastasis include pleural studding, hilar lymphadenopathy and the presence of extrapulmonary metastasis, as well as number of nodules, nodule size and nodule shape. Using a combination of these factors, we have developed an evidence-based multivariate decision tree to predict which nodules are malignant in these patients, which is 91 % accurate and 100 % sensitive for metastasis.
  
  
  CONCLUSIONS
  We propose a simplified clinical prediction algorithm to guide radiologists and oncologists in managing patients with breast cancer and incidental pulmonary nodules.
  
  
  CONCLUSIONS
  • Incidental pulmonary nodules are common on computed tomography in breast cancer patients. • Nodules present at baseline have a lower malignancy risk than incident nodules. • We present an evidence-based decision algorithm predicting which nodules are likely malignant. • This algorithm can help direct patient management.
authors: Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Ground Floor Founders Bldg., Philadelphia, PA, 19104, USA.
authors: Mark M
authors: MM
authors: Hammer
authors: Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Ground Floor Founders Bldg., Philadelphia, PA, 19104, USA. eduardo.barbosa@uphs.upenn.edu.
authors: Eduardo J
authors: EJ
authors: Mortani Barbosa
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000175:diagnosis* / Q000453:epidemiology; D009378:Neoplasms, Multiple Primary; D011237:Predictive Value of Tests; D015995:Prevalence; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Breast cancer; Incident lung nodule; Prediction algorithm; Prevalent lung nodule; Significance of lung nodules