Mark M Hammer1, Eduardo J Mortani Barbosa2. 1. 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. 2. 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.
Abstract
OBJECTIVES: 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. KEY POINTS: • 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.
OBJECTIVES: 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. KEY POINTS: • Incidental pulmonary nodules are common on computed tomography in breast cancerpatients. • 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.
Entities:
Keywords:
Breast cancer; Incident lung nodule; Prediction algorithm; Prevalent lung nodule; Significance of lung nodules
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