• Journal of the Korean Society of Radiology
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• v.79 no.5
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• pp.259-263
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• 2018

A 52-year-old male complained of a painless, firm, and slow-growing mass in his right breast outer portion. The chest CT revealed a 3.3 cm-sized oval shaped, microlobulated, mild enhancing mass. Ultrasound showed a microlobulated marginated heterogeneous hypoechoic mass with internal vascularity and calcifications in the mass. On the ultrasound-guided core needle biopsy, the mass was confirmed as a benign granular cell tumor (GCT). The patient transferred to another hospital and underwent surgical removal of the lesion. GCT of the breast is uncommon and mostly benign neoplasm to originate from Schwann cell. Clinical and radiologic features of GCTs, including CT and ultrasound images, mimic malignancy and make diagnosis of GCT more difficult. The CT images of GCTs are much rarely reported. Physicians and radiologists must be aware of radiologic characteristics of this rare benign tumor for male breast, to avoid misdiagnosis this tumor for breast malignancy and overtreat.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.867-879
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• 2021

Objective: To compare the screening performance of diffusion-weighted (DW) MRI and combined mammography and ultrasound (US) in detecting clinically occult contralateral breast cancer in women with newly diagnosed breast cancer. Materials and Methods: Between January 2017 and July 2018, 1148 women (mean age ± standard deviation, 53.2 ± 10.8 years) with unilateral breast cancer and no clinical abnormalities in the contralateral breast underwent 3T MRI, digital mammography, and radiologist-performed whole-breast US. In this retrospective study, three radiologists independently and blindly reviewed all DW MR images (b = 1000 s/mm² and apparent diffusion coefficient map) of the contralateral breast and assigned a Breast Imaging Reporting and Data System category. For combined mammography and US evaluation, prospectively assessed results were used. Using histopathology or 1-year follow-up as the reference standard, cancer detection rate and the patient percentage with cancers detected among all women recommended for tissue diagnosis (positive predictive value; PPV2) were compared. Results: Of the 30 cases of clinically occult contralateral cancers (13 invasive and 17 ductal carcinoma in situ [DCIS]), DW MRI detected 23 (76.7%) cases (11 invasive and 12 DCIS), whereas combined mammography and US detected 12 (40.0%, five invasive and seven DCIS) cases. All cancers detected by combined mammography and US, except two DCIS cases, were detected by DW MRI. The cancer detection rate of DW MRI (2.0%; 95% confidence interval [CI]: 1.3%, 3.0%) was higher than that of combined mammography and US (1.0%; 95% CI: 0.5%, 1.8%; p = 0.009). DW MRI showed higher PPV2 (42.1%; 95% CI: 26.3%, 59.2%) than combined mammography and US (18.5%; 95% CI: 9.9%, 30.0%; p = 0.001). Conclusion: In women with newly diagnosed breast cancer, DW MRI detected significantly more contralateral breast cancers with fewer biopsy recommendations than combined mammography and US.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.749-758
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• 2019

Objective: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared Results: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; p < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; p = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; p = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; p = 0.259). Conclusion: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.157-165
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• 2023

Ultrasound imaging uses sound waves of frequencies to cause physical actions such as reflection, absorption, refraction, and transmission at the edge between different tissues. Improvement is needed because there is a lot of noise due to the characteristics of the data generated from the ultrasound equipment, and it is difficult to grasp the shape of the tissue to be actually observed because the edge is vague. The edge enhancement method is used as a method to solve the case where the edge surface looks clumped due to a decrease in image quality. In this paper, as a method to strengthen the interface, the quality improvement was confirmed by strengthening the interface, which is the high-frequency part, in each image using an unsharpening mask and high boost. The mask filtering used for each image was evaluated by measuring PSNR and SNR. Abdominal, head, heart, liver, kidney, breast, and fetal images were obtained from Philips epiq5g and affiniti70g and Alpinion E-cube 15 ultrasound equipment. The program used to implement the algorithm was implemented with MATLAB R2022a of MathWorks. The unsharpening and high-boost mask array size was set to 3*3, and the laplacian filter, a spatial filter used to create outline-enhanced images, was applied equally to both masks. ImageJ program was used for quantitative evaluation of image quality. As a result of applying the mask filter to various ultrasound images, the subjective image quality showed that the overall contour lines of the image were clearly visible when unsharpening and high-boost mask were applied to the original image. When comparing the quantitative image quality, the image quality of the image to which the unsharpening mask and the high boost mask were applied was evaluated higher than that of the original image. In the portal vein, head, gallbladder, and kidney images, the SNR, PSNR, RMSE and MAE of the image to which the high-boost mask was applied were measured to be high. Conversely, for images of the heart, breast, and fetus, SNR, PSNR, RMSE and MAE values were measured as images with the unsharpening mask applied. It is thought that using the optimal mask according to the image will help to improve the image quality, and the contour information was provided to improve the image quality.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.6
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• pp.13-21
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• 2016

Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.

• Parasites, Hosts and Diseases
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• v.55 no.4
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• pp.421-424
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• 2017

Sparganosis is a parasitic infection caused by the sparganum, the plercercoid of the genus Spirometra. The preoperative diagnosis of breast sparganosis is difficult in most cases because it is a rare parasitic infection less than 2% of all cases. We report a 62-year-old woman case of breast sparganosis that were confirmed by surgical removal of worms from the right breast. The radiologic images of the patient also revealed characteristic features of breast sparganosis. The patient described the migrating palpable breast mass, which strongly suggested the possibility of breast sparganosis. The treatment of choice and confirmative diagnosis for sparganosis are complete surgical extraction of the sparganum irrespective of infected site. Inspection of the mass site with detailed medical history and radiological examinations are important for preoperative diagnosis of sparganosis patients.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8619-8623
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• 2016

Background: Breast cancer is a common disorder in women, constituting one of the main causes of death all over the world. The purpose of this study was to determine the diagnostic value of the breast tissue diseases by the help of thermography. Materials and Methods: In this paper, we applied non-contact infrared camera, INFREC R500 for evaluating the capabilities of thermography. The study was conducted on 60 patients suspected of breast disease, who were referred to Imam Khomeini Imaging Center. Information obtained from the questionnaires and clinical examinations along with the obtained diagnostic results from ultrasound images, biopsies and thermography, were analyzed. The results indicated that the use of thermography as well as the asymmetry technique is useful in identifying hypoechoic as well as cystic masses. It should be noted that the patient should not suffer from breast discharge. Results: The accuracy of asymmetry technique identification is respectively 91/89% and 92/30%. Also the accuracy of the exact location of identification is on the 61/53% and 75%. The approach also proved effective in identifying heterogeneous lesions, fibroadenomas, and intraductal masses, but not ISO-echoes and calcified masses. Conclusions: According to the results of the investigation, thermography may be useful in the initial screening and supplementation of diagnostic procedures due to its safety (its non-radiation properties), low cost and the good recognition of breast tissue disease.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.45-52
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• 2021

PURPOSE: This study examined the imaging procedure of pectoralis minor muscle thickness and assessed the intra- and inter-rater reliability of the muscle thickness measured by two raters using rehabilitative ultrasound imaging (RUSI) in healthy individuals. METHODS: Fifteen participants (aged 21 - 28, seven females, and eight males) were involved in the study. The primary rater palpated the coracoid process and the fourth rib, defined as the width of the index finger lateral to the sternum to avoid breast tissues, and lined the two landmarks. The second examiner checked 1 / 3 (1st point) and 1 / 2 (2nd point) of the line length as measurement points. The two raters obtained right side muscle images of the participants at a standardized sitting position using RUSI with a 7.5 MHz linear transducer at 40mm depth. For intra-rater reliability, the principal rater took three images per point and tried to take one more with an interval. For the inter-rater reliability, the other rater performed the same tasks as the principal rater on the same day. The reliability was analyzed using the intra-class correlation coefficient (ICC), the standard error of the measurement (SEM), and Bland and Altman plots. RESULTS: The reliability at all points was excellent for the same rater (ICC_3,1 = .973 - .978, SEM = .042 - .046), and between raters (ICC_2,1 = .939 - .959, SEM = .059 - .097). CONCLUSION: These findings show that the RUSI could be reliable for examining the pectoralis minor muscle thickness in healthy individuals at all measurement sites.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.919-927
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• 2023

Elastography utilizes the fact that the tissue of a malignant tumor is harder than that of a benign tumor and increases the specificity of diagnosis according to the elastic modulus of the tumor, helping to reduce unnecessary biopsies. However, the reliability of elastography can be influenced by the equipment used and the examiner's skills. In this study, the researchers analyzed the reproducibility of elastography by evaluating phantom images when measuring the elasticity values repeatedly. Phantoms were created using silicone and gelatin with different levels of stiffness, and they were inserted at varying depths from the surface. The elasticity values were measured using shear wave elastography. The study aimed to determine whether the reproducibility of elasticity values remains consistent depending on the stiffness and depth of the lesions. The experimental results showed that there was no statistically significant correlation between the elasticity values obtained through shear wave elastography and the depth or stiffness of the lesions. However, in the lesions with the lowest stiffness, the elasticity values were statistically significant (p<0.001) and showed a high correlation with the depth of the lesions. Although there were variations in the measured elasticity values based on the differences in lesion stiffness and depth, these differences did not significantly impact the diagnosis. Therefore, shear wave elastography remains a reliable diagnostic method, and it is suggested that it can be helpful in the diagnosis of breast lesions.