• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.3-14
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• 2023

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.889-902
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• 2021

Purpose To assess the diagnostic performance of contrast-enhanced ultrasound (CEUS) for additional MR-detected enhancing lesions and to determine whether or not kinetic pattern results comparable to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can be obtained using the quantitative analysis of CEUS. Materials and Methods In this single-center prospective study, a total of 71 additional MR-detected breast lesions were included. CEUS examination was performed, and lesions were categorized according to the Breast Imaging-Reporting and Data System (BI-RADS). The sensitivity, specificity, and diagnostic accuracy of CEUS were calculated by comparing the BI-RADS category to the final pathology results. The degree of agreement between CEUS and DCE-MRI kinetic patterns was evaluated using weighted kappa. Results On CEUS, 46 lesions were assigned as BI-RADS category 4B, 4C, or 5, while 25 lesions category 3 or 4A. The diagnostic performance of CEUS for enhancing lesions on DCE-MRI was excellent, with 84.9% sensitivity, 94.4% specificity, and 97.8% positive predictive value. A total of 57/71 (80%) lesions had correlating kinetic patterns and showed good agreement (weighted kappa = 0.66) between CEUS and DCE-MRI. Benign lesions showed excellent agreement (weighted kappa = 0.84), and invasive ductal carcinoma (IDC) showed good agreement (weighted kappa = 0.69). Conclusion The diagnostic performance of CEUS for additional MR-detected breast lesions was excellent. Accurate kinetic pattern assessment, fairly comparable to DCE-MRI, can be obtained for benign and IDC lesions using CEUS.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.36-41
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• 2013

Decision-making is to extract information that can be executed in the future, it refers to the process of discovering a new data model that is induced in the data. In other words, it is to find out the information to peel off to find the vein to catch the relationship between the hidden patterns in data. The information found here, is a process of finding the relationship between the useful patterns by applying modeling techniques and sophisticated statistical analysis of the data. It is called data mining which is a key technology for marketing database. Therefore, research for cluster analysis of the current is performed actively, which is capable of extracting information on the basis of the large data set without a clear criterion. The EM and K-means methods are used a lot in particular, how the result values of evaluating are come out in experiments, which are depending on the size of the data by the type of distance-based and probability-based data analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7731-7736
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• 2013

Aim: To compare the agreement of screening breast mammography plus ultrasound and reviewed mammography alone in asymptomatic women. Materials and Methods: All breast imaging data were obtained for women who presented for routine medical checkup at National Cancer Institute (NCI), Thailand from January 2010 to June 2013. A radiologist performed masked interpretations of selected mammographic images retrieved from the computer imaging database. Previous mammography, ultrasound reports and clinical data were blinded before film re-interpretation. Kappa values were calculated to assess the agreement between BIRADS assessment category and BIRADS classification of density obtained from the mammography with ultrasound in imaging database and reviewed mammography alone. Results: Regarding BIRADS assessment category, concordance between the two interpretations were good. Observed agreement was 96.1%. There was moderate agreement in which the Kappa value was 0.58% (95%CI; 0.45, 0.87). The agreement of BI-RADS classification of density was substantial, with a Kappa value of 0.60 (95%CI; 0.54, 0.66). Different results were obtained when a subgroup of patients aged ${\geq}60$ years were analyzed. In women in this group, observed agreement was 97.6%. There was also substantial agreement in which the Kappa value was 0.74% (95%CI; 0.49, 0.98). Conclusions: The present study revealed that concordance between mammography plus ultrasound and reviewed mammography alone in asymptomatic women is good. However, there is just moderate agreement which can be enhanced if age-targeted breast imaging is performed. Substantial agreement can be achieved in women aged ${\geq}60$. Adjunctive breast ultrasound is less important in women in this group.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.135-149
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• 2022

Purpose: To assess clinically significant imaging findings of malignant intramammary lymph nodes (IMLNs) in breast cancer patients and to evaluate their diagnostic performance in predicting malignant IMLN. Materials and Methods: A total of 110 cases with IMLN of BI-RADS category 3 or more, not typical benign IMLN, in MR of breast cancer patients between January 2016 and January 2021 were retrospectively reviewed. After excluding 33 cases, 77 cases were finally included. Among them, 58 and 19 were confirmed as benign and malignant, respectively. Qualitative and quantitative MR imaging features of the IMLN were retrospectively analyzed. Sizes and final assessment categories of IMLN on MRI, mammography, and ultrasound were reviewed. Diagnostic performances of imaging features on MRI, mammography, and ultrasound were then evaluated. Results: For qualitative MR features, shape, margin, and preserved central hilum were significantly different between benign and malignant groups (P < 0.05). For quantitative MR features, long diameter over 6 mm, short diameter over 4 mm, and cortical thickening over 3 mm showed high sensitivities in predicting malignant IMLNs (89.5%, 94.7%, and 100%, respectively). Size exceeding 1 cm showed high specificity and accuracy in predicting malignant IMLN on MR, mammography, and ultrasound (91.4% and 80.5%; 96.6% and 79.25; 98.3% and 80.5%, respectively). Conclusion: Various MR imaging features and size can be helpful for predicting malignant IMLN in breast cancer patients.

• Investigative Magnetic Resonance Imaging
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• v.13 no.2
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• pp.183-189
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• 2009

Purpose : To evaluate the usefulness of three-dimensional (3D) maximal intensity projection (MIP) reconstruction method in breast MRI. Materials and Methods : Total 54 breasts of consecutive 27 patients were examined by breast MRI. Breast MRI was performed using GE Signa Excite Twin speed (GE medical system, Wisconsin, USA) 1.5T. We obtained routine breast MR images including axial T2WI, T1WI, sagittal T1FS, dynamic contrast-enhanced T1FS, and subtraction images. 3D MIP reconstruction images were obtained as follows; subtraction images were obtained using TIPS and early stage of contrast-enhanced TIPS images. And then 3D MIP images were obtained using the subtraction images through advantage workstation (GE Medical system). We detected and analyzed the lesions in the 3D MIP and routine MRI images according to ACR $BIRADS^{(R)}$ MRI lexicon. And then we compared the findings of 3D MIP and those of routine breast MR images and evaluated whether 3D MIP had additional information comparing to routine MR images. Results : 3D MIP images detect the 43 of 56 masses found on routine MR images (76.8%). In non-mass like enhancement, 3D MIP detected 17 of 20 lesions (85 %). And there were one hundred sixty nine foci at 3D MIP images and one hundred nine foci at routine MR images. 3D MIP images detected 14 of 23 category 3 lesions (60.9%), 11 of 16 category 4 lesions (68.87%), 28 of 28 Category 5 lesions (100%). In analyzing the enhancing lesions at 3D MIP images, assessment categories of the lesions were correlated as the results at routine MR images (p-value < 0.0001). 3D MIP detected additional two daughter nodules that were descriped foci at routine MR images and additional one nodule that was not detected at routine MR images. Conclusion : 3D MIP image has some limitations but is useful as additional image of routine breast MR Images.