Dataset Information

Low-Dose, Contrast-Enhanced Mammography Compared to Contrast-Enhanced Breast MRI: A Feasibility Study.

ABSTRACT: Contrast-enhanced MRI (CE-MRI) is the most sensitive technique for breast cancer detection. Contrast-enhanced mammography (CEM) is emerging as a possible alternative to CE-MRI. PURPOSE:To evaluate the diagnostic performance of a low radiation dose contrast-enhanced mammography (L-CEM) in women with suspicious findings on conventional imaging compared to CE-MRI of the breast. STUDY TYPE:Prospective, single center. POPULATION:Women with suspicious findings on mammography, tomosynthesis, or ultrasound, and no contraindications for L-CEM or CE-MRI. Eighty women were included. FIELD STRENGTH/SEQUENCE:1.5 and 3T?CE-MRI, standard protocol for breast, with dedicated coils, according to international guidelines. L-CEM was performed using a dedicated prototype. ASSESSMENT:Three, off-site, blinded readers evaluated the images according to the BI-RADS lexicon in a randomized order, each in two separate reading sessions. Histology served as a gold standard. STATISTICAL TEST:Lesion detection rate, sensitivity, specificity, and negative and positive predictive values (NPV, PPV) were calculated and compared with multivariate statistics. RESULTS:Included were 80 women (mean age, 54.3?years ±11.2 standard deviation) with 93 lesions (32 benign, 61 malignant). The detection rate was significantly higher with CE-MRI (92.5-94.6%; L-CEM 79.6-91.4%, P = 0.014). Sensitivity (L-CEM 65.6-90.2%; CE-MRI 83.6-93.4%, P = 0.086) and NPV (L-CEM 59.6-71.4%; CE-MRI 63.0-76.5%, P = 0.780) did not differ between the modalities. Specificity (L-CEM 46.9-96.9%; CE-MRI 37.5-53.1%, P = 0.001) and PPV (L-CEM 76.4-97.6%; CE-MRI 73.3-77.3%, P = 0.007) were significantly higher with L-CEM. Variations between readers were significant for sensitivity and NPV. The accuracy of L-CEM was as good as CE-MRI (75.3-76.3% vs. 72.0-75.3%, P = 0.514). DATA CONCLUSION:L-CEM showed a high sensitivity and accuracy in women with suspicious findings on conventional imaging. Compared to CE-MRI, L-CEM has the potential to increase specificity and PPV. L-CEM might help to reduce false-positive biopsies while obtaining sensitivity comparable to that of CE-MRI LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:589-595.

SUBMITTER: Clauser P

PROVIDER: S-EPMC7496227 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Low-Dose, Contrast-Enhanced Mammography Compared to Contrast-Enhanced Breast MRI: A Feasibility Study.

Clauser Paola P Baltzer Pascal A T PAT Kapetas Panagiotis P Hoernig Mathias M Weber Michael M Leone Federica F Bernathova Maria M Helbich Thomas H TH

Journal of magnetic resonance imaging : JMRI 20200214 2

Contrast-enhanced MRI (CE-MRI) is the most sensitive technique for breast cancer detection. Contrast-enhanced mammography (CEM) is emerging as a possible alternative to CE-MRI.<h4>Purpose</h4>To evaluate the diagnostic performance of a low radiation dose contrast-enhanced mammography (L-CEM) in women with suspicious findings on conventional imaging compared to CE-MRI of the breast.<h4>Study type</h4>Prospective, single center.<h4>Population</h4>Women with suspicious findings on mammography, tomo ...[more]

PMID: 32061002

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Project description:PurposeTo investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade.ProceduresThis retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference.ResultsRadiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI).ConclusionsRadiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

Project description:Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions. Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal-Wallis rank-sum test. Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22-70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873-0.989) for HE, 0.897 (95% CI: 0.807-0.981) for LE, 0.882 (95% CI: 0.825-0.987) for DES images and 0.960 (95% CI: 0.910-0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P < 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P < 0.05) and testing sets (0.866 and 0.862, respectively; all P < 0.05). Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

Dataset Information

Low-Dose, Contrast-Enhanced Mammography Compared to Contrast-Enhanced Breast MRI: A Feasibility Study.

Publications

Low-Dose, Contrast-Enhanced Mammography Compared to Contrast-Enhanced Breast MRI: A Feasibility Study.

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