Magnetic resonance imaging evaluation of residual ductal carcinoma in situ following preoperative chemotherapy in breast cancer patients

Choi, Hei Kyung; Cho, Nariya; Moon, Woo Kyung; Im, Seock Ah; Han, Wonshik; Noh, Dong Young

doi:10.1016/j.ejrad.2011.01.013

Cited by 14 publications

(19 citation statements)

References 20 publications

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“…The results identified that the proposed method demonstrated improved performance in distinguishing benign from malignant lesions compared with the traditional method. For TIC classification, although the sensitivity of the traditional method was high, its specificity was low, which is in agreement with a number of previous studies (20)(21)(22)(23)(24)(25). Low specificity in DCE-MRI diagnosis may lead to low accuracy.…”

Section: Discussionsupporting

confidence: 88%

Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis

2019

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The aim of the current study was to develop a semi-automatic and quantitative method for the analysis of a time-intensity curve (TIC) from breast dynamic contrastenhanced magnetic resonance imaging. The performance of the proposed method, based on the level set segmentation algorithm, was evaluated by comparison with the traditional method. In the traditional method, the lesion area is delineated manually and the corresponding mean TIC is classified subjectively as one of three washout patterns. In addition, only one quantitative parameter, the maximum slope of increase (MSI), is calculated. In the proposed method, the lesion region was determined semi-automatically and the corresponding mean TIC was categorized quantitatively. In addition to MSI, a number of quantitative parameters were derived from the mean TIC and lesion area, including signal intensity slope (SI slope), initial percentage of enhancement (E initial), percentage of peak enhancement (E peak), early signal enhancement ratio (ESER) and second enhancement percentage (SEP). Wilcoxon signed-rank test and receiver operating characteristic analyses were performed for statistical analysis. For TIC categorization the accuracy was 61.54% for the traditional method and 82.05% for the proposed method. Using the proposed method, mean curve accuracies were 84.0% for SI slope , 66.7% for MSI, 66.0% for E initial , 66.0% for E peak , 68.0% for ESER and 44.9% for SEP. In the lesion region, the accuracies for the aforementioned parameters were 80.8, 65.4, 66.7, 62.2, 69.2 and 57.1%, respectively. Accuracy of the MSI value derived from the traditional method was 63.4%. Compared with the traditional method, the proposed semi-automatic method in the current study may provide results with a higher accuracy to differentiate benign and malignant lesions. Therefore, the proposed method should be considered as a supplementary tool for the diagnosis of breast lesions.

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Section: Discussionsupporting

confidence: 88%

Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis

2019

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“…Detection of DCIS by MRI is likely to improve in future and can become a regular indication of post-NAC MRI, however, further studies are needed to assess MRI efficacy in assessing DCIS after NAC. [192021]…”

Section: Discussionmentioning

confidence: 99%

Accuracy of MRI for prediction of response to neo-adjuvant chemotherapy in triple negative breast cancer compared to other subtypes of breast cancer

Bansal

Darisetty

2016

Indian Journal of Radiology and Imaging

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Purpose:The aim of this study was to compare the accuracy of magnetic resonance imaging (MRI) for the prediction of response to neo-adjuvant chemotherapy in triple negative (TN) breast cancer, with respect to other subtypes.Materials and Methods:There were a total of 1610 breast cancers diagnosed between March 2009 and August 2014, out of which 82 patients underwent MRI before and after neo-adjuvant chemotherapy but just before surgery. TN cancers were analyzed with respect to others subtypes. Accuracy of MRI for prediction of pathological complete response was compared between different subtypes by obtaining receiver operating characteristic (ROC) curves. The Statistical Package for the Social Sciences version 21 was used for all data analysis, with P value of 0.05 as statistically significant.Results:Out of 82 patients, 29 were luminal (HR+/HER2−), 23 were TN (HR−, HER2−), 11 were HER2 positive (HR−, HER2+), and 19 were of hybrid subtype (HR+/HER2+). TN cancers presented as masses on the pre-chemotherapy MRI scan, were grade 3 on histopathology, and showed concentric shrinkage following chemotherapy. TN cancers were more likely to have both imaging and pathological complete response following chemotherapy (P = 0.055) in contrast to luminal cancers, which show residual cancer. ROC curves were constructed for the prediction of pathological complete response with MRI. For the TN subgroup, MR had a sensitivity of 0.745 and specificity of 0.700 (P = 0.035), with an area under curve of 0.745 (95% confidence interval: 0.526–0.965), which was significantly better compared to other subtypes.Conclusion:TN breast cancers present as masses and show concentric shrinkage following chemotherapy. MRI is most accurate in predicting response to chemotherapy in the TN group, compared to others subtypes. MRI underestimates residual disease in luminal cancers.

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“…If the case size was changed, both the optimal cutoff value and accuracy rate might be changed accordingly. Second, both the manual method and the semi-automatic method were performed only once, hence the inter- and intra-observer variabilities were not investigated [26], although we assumed that the semi-automatic method might offer greater reproducibility by virtue of its simplicity and quantification, especially when deployed across multiple sites in a large-scale clinical trial [33,48]. Third, in this study, we only analyzed the TICs from breast DCE-MRI, whereas the morphologic features of the lesions were not utilized for breast diagnosis, which could have further improved the diagnostic accuracy of breast DCE-MRI [17,36].…”

Section: Discussionmentioning

confidence: 99%

Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI

Yin

Yang

Han

et al. 2015

J Exp Clin Cancer Res

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Magnetic resonance imaging evaluation of residual ductal carcinoma in situ following preoperative chemotherapy in breast cancer patients

Cited by 14 publications

References 20 publications

Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis

Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis

Accuracy of MRI for prediction of response to neo-adjuvant chemotherapy in triple negative breast cancer compared to other subtypes of breast cancer

Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI

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