He Dongfeng1, Ma Daqing, Jin Erhu. 1. Department of Radiology, The Affiliated Beijing Friendship Hospital of Capital Medical University of China, Beijing, China.
Abstract
BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential of predicting response to neoadjuvant chemotherapy for patients with breast cancer. However, most of these studies focused on evaluating hot-spot characteristics. To thoroughly reflect tumor status, the cold spot and heterogeneity characteristics should also be evaluated. PATIENTS AND METHODS: DCE-MRIs from 60 patients newly diagnosed with primary invasive breast cancer were reviewed. Kinetic parameters (including cold spot, hot spot, and heterogeneity parameters) derived from DCE-MRI data were used to describe cold spot, hot spot, and heterogeneity features. Patients with a pathologic complete response (pCR) or a ductal carcinoma in situ with microinvasion after chemotherapy were categorized into the pCR group. Pretreatment kinetic parameters in the pCR and non-pCR groups were compared by using univariate tests. Binary logistic regression analysis was used to identify the independent predictors for pCR. The best cutoff value of the independent predictor at pretreatment, with which to differentiate between patients who had a pCR and a non-pCR, was calculated by using receiver operating characteristic curve analysis. RESULTS: After chemotherapy, 10 (16.7%) patients were categorized into the pCR group and 50 (83.3%) into non-pCR group. Multivariate analysis showed that pretreatment washout slope at a cold spot (washout(C)) was the only significant and independent predictor of pCR (β = 26.128; P = .005). The best pretreatment washout(C) cutoff value with which to differentiate between patients who had pCR and those with non-pCR was 0.0277, which yielded a sensitivity of 80.0% (95% CI, 44.4%-97.5%) and a specificity of 74.0% (95% CI, 59.7%-85.4%). CONCLUSION: Washout(C) may be used as a predictor for pCR in patients with breast cancer who undergo neoadjuvant chemotherapy. Copyright Â
BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential of predicting response to neoadjuvant chemotherapy for patients with breast cancer. However, most of these studies focused on evaluating hot-spot characteristics. To thoroughly reflect tumor status, the cold spot and heterogeneity characteristics should also be evaluated. PATIENTS AND METHODS: DCE-MRIs from 60 patients newly diagnosed with primary invasive breast cancer were reviewed. Kinetic parameters (including cold spot, hot spot, and heterogeneity parameters) derived from DCE-MRI data were used to describe cold spot, hot spot, and heterogeneity features. Patients with a pathologic complete response (pCR) or a ductal carcinoma in situ with microinvasion after chemotherapy were categorized into the pCR group. Pretreatment kinetic parameters in the pCR and non-pCR groups were compared by using univariate tests. Binary logistic regression analysis was used to identify the independent predictors for pCR. The best cutoff value of the independent predictor at pretreatment, with which to differentiate between patients who had a pCR and a non-pCR, was calculated by using receiver operating characteristic curve analysis. RESULTS: After chemotherapy, 10 (16.7%) patients were categorized into the pCR group and 50 (83.3%) into non-pCR group. Multivariate analysis showed that pretreatment washout slope at a cold spot (washout(C)) was the only significant and independent predictor of pCR (β = 26.128; P = .005). The best pretreatment washout(C) cutoff value with which to differentiate between patients who had pCR and those with non-pCR was 0.0277, which yielded a sensitivity of 80.0% (95% CI, 44.4%-97.5%) and a specificity of 74.0% (95% CI, 59.7%-85.4%). CONCLUSION: Washout(C) may be used as a predictor for pCR in patients with breast cancer who undergo neoadjuvant chemotherapy. Copyright Â
Authors: M B I Lobbes; R Prevos; M Smidt; V C G Tjan-Heijnen; M van Goethem; R Schipper; R G Beets-Tan; J E Wildberger Journal: Insights Imaging Date: 2013-01-29
Authors: A Evans; S Armstrong; P Whelehan; K Thomson; P Rauchhaus; C Purdie; L Jordan; L Jones; A Thompson; S Vinnicombe Journal: Br J Cancer Date: 2013-10-29 Impact factor: 7.640