• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10739-10743
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• 2015

The present study aimed at evaluating and comparing the diagnostic performance of B-mode ultrasound (US), elastography score (ES), and strain ratio (SR) for the differentiation of breast lesions. This retrospective study enrolled 431 lesions from 417 in-hospital patients. All patients were examined with both conventional ultrasound and elastography. Two experienced radiologists reviewed ultrasound and elasticity images. The histopathologic result obtained from ultrasound-guided core biopsy or operation excisions were used as the reference standard. Pathologic examination revealed 276 malignant lesions (64%) and 155 benign lesions (36%). A cut-off point of 4.15 (area under the curve, 0.891) allowed significant differentiation of malignant and benign lesions. ROC (receiver-operating characteristic) curves showed a higher value for combination of B-mode ultrasound and elastography for the diagnosis of breast lesions. Conventional ultrasound combined elastography showed high sensitivity, specificity, and accuracy for group II lesions (10mm${\leq}20mm$). Elastography combined with conventional ultrasound show high specificity and accuracy for differentiation of benign and malignant breast lesions. Elastography is particularly important for the diagnosis of BI-RADS 4 and small breast lesions.

• Korean Journal of Agricultural Science
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• v.46 no.3
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• pp.539-548
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• 2019

This study investigated the influence of ultrasound treatment on the quality properties of chicken breast meat and the broth from Korean chicken soup (Baeksuk). In this study, the internal temperature, malondialdehyde content, textural profile, color, dry matter, protein content, phenolic content and sensory properties of chicken breast meat broth from chicken soup with ultrasound treatment were analyzed. The chicken, plants, salt, and water were vacuum packaged in a retort pouch. The chicken soup was manufactured with ultrasound treatment (45 kHz and $1.6W\;cm^{-2}$) in a water bath at $85^{\circ}C$. The texture properties, color, and lipid oxidation of the chicken breast meat from the chicken soup were not affected by the ultrasound treatment. There was no significant difference in the lipid oxidation in the broth of the chicken soup between the control and ultrasound treatment. The dry matter and crude protein contents of the broth were significantly increased by the ultrasound treatment. The broth flavor of the chicken soup manufactured with the ultrasound treatment received a higher score than that of the control in the sensory analysis. There were no differences in the sensory properties of the chicken breast meat from the chicken soup between the control and ultrasound treatment Therefore, the broth quality of the chicken soup can be improved by heating with ultrasound treatment. Additionally, to apply ultrasound technology to the production chicken breast meat and the broth from chicken soup, it is necessary to further study the quality characteristics of the breast meat and broth according to various frequencies and strengths.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.15-24
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• 2020

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8057-8062
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• 2014

Purpose: The aim of the study was to evaluate the correlation of ultrasound features with breast cancer molecular status. Materials and Methods: A retrospective review was performed of ultrasound findings in 263 patients diagnosed with breast invasive ductal carcinoma for comparison with immunohistochemistric results were obtained from each lesion. Relationships between ultrasound findings and molecular status were investigated by using multiple regression analysis by means of stepwise logistic regression. Differences in ultrasound criteria were assessed among women with different molecular status. Results: ER positivity was associated with small size, lobulate, angular or spiculated margin contours, absence of calcification, posterior tumor shadowing and low elasticity score; PR positivity was associated with small size, lobulate or angular or spiculated margin contours and absence of calcification; HER2 positivity was associated with presence of calcification and absence of any echogenic halo. The calculated models of predicted molecular status were accurate and discriminating with AUCs of 0.78, 0.74, and 0.74, respectively. Conclusions: Breast cnacer ultrasound features show some correlation with the molecular status. These models may help to expand the scope of ultrasound in predicting tumor biology.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.124-131
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• 2024

Breast cancer classification using ultrasound, while widely employed, faces challenges due to its relatively low predictive value arising from significant overlap in characteristics between benign and malignant lesions, as well as operator-dependency. To alleviate these challenges and reduce dependency on radiologist interpretation, the implementation of automatic breast cancer classification in ultrasound image can be helpful. To deal with this problem, we propose a semi-supervised deep learning framework for breast cancer classification. In the proposed method, we could achieve reasonable performance utilizing less than 50% of the training data for supervised learning in comparison to when we utilized a 100% labeled dataset for training. Though it requires more modification, this methodology may be able to alleviate the time-consuming annotation burden on radiologists by reducing the number of annotation, contributing to a more efficient and effective breast cancer detection process in ultrasound images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.25-32
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• 2020

Objective: To comparatively evaluate the scan coverage and diagnostic performance of the two-view scan technique (2-VST) of the automated breast ultrasound system (ABUS) versus the conventional three-view scan technique (3-VST) in women with small breasts. Materials and Methods: Between March 2016 and May 2017, 136 asymptomatic women with small breasts (bra cup size A) suitable for 2-VST were enrolled. Subsequently, 272 breasts were subjected to bilateral whole-breast ultrasound examinations using ABUS and the hand-held ultrasound system (HHUS). During ABUS image acquisition, one breast was scanned with 2-VST, while the other breast was scanned with 3-VST. In each breast, the breast coverage and visibility of the HHUS detected lesions on ABUS were assessed. The sensitivity and specificity of ABUS were compared between 2-VST and 3-VST. Results: Among 136 breasts, eight cases of breast cancer were detected by 2-VST, and 10 cases of breast cancer were detected by 3-VST. The breast coverage was satisfactory in 94.1% and 91.9% of cases under 2-VST and 3-VST, respectively (p = 0.318). All HHUS-detected lesions were visible on the ABUS images regardless of the scan technique. The sensitivities and specificities were similar between 2-VST and 3-VST (100% [8/8] vs. 100% [10/10], and 97.7% [125/128] vs. 95.2% [120/126], respectively), with no significant difference (p > 0.05). Conclusion: 2-VST of ABUS achieved comparable scan coverage and diagnostic performance to that of conventional 3-VST in women with small breasts.