PURPOSE: To determine if magnetic resonance (MR) imaging can be used to predict axillary lymph node status in patients with breast cancer. MATERIALS AND METHODS: Fifty-one women with primary invasive breast cancer underwent dynamic contrast material-enhanced MR imaging of the breast Region-of-interest (ROI) analysis was performed on parametric images obtained with kinetic modeling of the data. Large and automated ROIs were selected. Typical enhancement ratios that represented the relative increase in mean pixel signal intensity were calculated for each ROI. Stepwise logistic regression analysis was applied to identify prognostic factors of axillary node status. Receiver operating characteristic analysis was performed and a Brier score and calibration curve were calculated to assess the diagnostic efficacy and predictive capability of the logistic regression model. RESULTS: The maximum enhancement ratio of the automated ROI was found to be the strongest predictor of node status (P < .001). Patient age (P = .007) and ROI size (P = .045) were also significant predictor variables. The model showed good accuracy (area beneath the fitted binormal receiver operating characteristic curve [Az] = 0.90; Brier score, 0.133). In 12 (24%) of the patients, a less than 5% or greater than 95% probability of positive-node status was correctly identified. CONCLUSION: The suggested predictive model may decrease the need for surgical staging of the axilla in patients with breast cancer.
PURPOSE: To determine if magnetic resonance (MR) imaging can be used to predict axillary lymph node status in patients with breast cancer. MATERIALS AND METHODS: Fifty-one women with primary invasive breast cancer underwent dynamic contrast material-enhanced MR imaging of the breast Region-of-interest (ROI) analysis was performed on parametric images obtained with kinetic modeling of the data. Large and automated ROIs were selected. Typical enhancement ratios that represented the relative increase in mean pixel signal intensity were calculated for each ROI. Stepwise logistic regression analysis was applied to identify prognostic factors of axillary node status. Receiver operating characteristic analysis was performed and a Brier score and calibration curve were calculated to assess the diagnostic efficacy and predictive capability of the logistic regression model. RESULTS: The maximum enhancement ratio of the automated ROI was found to be the strongest predictor of node status (P < .001). Patient age (P = .007) and ROI size (P = .045) were also significant predictor variables. The model showed good accuracy (area beneath the fitted binormal receiver operating characteristic curve [Az] = 0.90; Brier score, 0.133). In 12 (24%) of the patients, a less than 5% or greater than 95% probability of positive-node status was correctly identified. CONCLUSION: The suggested predictive model may decrease the need for surgical staging of the axilla in patients with breast cancer.
Authors: Christopher Loiselle; Peter R Eby; Janice N Kim; Kristine E Calhoun; Kimberly H Allison; Vijayakrishna K Gadi; Sue Peacock; Barry E Storer; David A Mankoff; Savannah C Partridge; Constance D Lehman Journal: Acad Radiol Date: 2014-01 Impact factor: 3.173
Authors: Elizabeth S Burnside; Karen Drukker; Hui Li; Ermelinda Bonaccio; Margarita Zuley; Marie Ganott; Jose M Net; Elizabeth J Sutton; Kathleen R Brandt; Gary J Whitman; Suzanne D Conzen; Li Lan; Yuan Ji; Yitan Zhu; Carl C Jaffe; Erich P Huang; John B Freymann; Justin S Kirby; Elizabeth A Morris; Maryellen L Giger Journal: Cancer Date: 2015-11-30 Impact factor: 6.860
Authors: Eun Young Ko; Sang Hoon Lee; Hak Hee Kim; Sung Moon Kim; Myung Jin Shin; Namkug Kim; Gyungyub Gong Journal: Korean J Radiol Date: 2008 May-Jun Impact factor: 3.500
Authors: Sebastian Wojcinski; Jennifer Dupont; Werner Schmidt; Michael Cassel; Peter Hillemanns Journal: BMC Med Imaging Date: 2012-12-19 Impact factor: 1.930