• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.1
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• pp.42-46
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• 2016

Purpose Mammography is the most widely used scan for the early diagnosis since it is possible to observe the anatomy of the breast. however, The sensitivity is markedly reduced in high-risk patients with dense breast. Molecular Breast Imaging (MBI) sacn is possible to get the high resolution functional imaging, and This new neclear medicine technique get the more improved diagnostic information through It is useful for confirmation of tumor's location in dense breast. The purpose of this study is to evaluate the usefulness of MBI for tumor diagnosis in patients with dense breast. Materials and Methods We investigated 10 patients female breast cancer with dense breast type who had visited the hospital from September 1st to Octorber 10th, 2015. The patients underwent both MBI and Mammography. MBI (Discovery 750B; General Electric Healthcare, USA) scan was 99mTc-MIBI injected with 20 mCi on the opposite side of the arm with the lesions, after 20 minutes, gained bilateral breast CC (CranioCaudal), MLO (Medio Lateral Oblique) View. Mammography was also conducted in the same posture. MBI and Mammography images were compared to evaluate the sensitivity and specificity of each case utilizing both image and two images in blind tests. Results The results of the blind test for breast cancer showed that the sensitivity of Mammography, MBI scan was 63%, 89%, respectively, and that their specificity was 38%, 87%, respectively. Using both the Mammography and MBI scan was Sensitivity 92%, specificity 90%. Conclusion This research has found that, The tumor of dense tissue that can not easily distinguishable in Mammography is possible to more accurate diagnosis since It is easy to visually evaluation. But MBI sacn has difficulty imaging microcalcificatons, If used in conjunction with mammography it is thought to give provide more diagnostic information.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.779-792
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• 2020

Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3037-3040
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• 2013

Introduction: Mammography is the most basic modality in breast cancer imaging. However, the overlap of breast tissue depicted on conventional two-dimensional mammography (2DMMG) may create significant obstacles to detecting abnormalities, especially in dense or heterogeneously dense breasts. In three-dimensional digital breast tomosynthesis (3DBT), tomographic images of the breast are reconstructed from multiple projections acquired at different angles. It has reported that this technology allows the generation of 3D data, therefore overcoming the limitations of conventional 2DMMG for Western women. We assessed the detectability of lesions by conventional 2DMMG and 3DBT in diagnosis of breast cancer for Japanese women. Methods: The subjects were 195 breasts of 99 patients (median age of 48 years, range 34~82 years) that had been pathologically diagnosed with breast cancer from December 20, 2010 through March 31, 2011. Both conventional 2DMMG and 3DBT imaging were performed for all patients. Detectability of lesions was assessed based on differences in category class. Results: Of the affected breasts, 77 (75.5%) had lesions assigned to the same categories by 2DMMG and 3DBT. For 24 (23.5%) lesions, the category increased in 3DBT indicating improvement in diagnostic performance compared to 2DMMG. 3DBT improved diagnostic sensitivity for patients with mass, focal asymmetric density (FAD), and architectural distortion. However, 3DBT was not statistically superior in diagnosis of the presence or absence of calcification. Conclusions: In this study, 3DBT was superior in diagnosing lesions in form of mass, FAD, and/or architectural distortion. 3DBT is a novel technique that may provide a breakthrough in solving the difficulties of diagnosis caused by parenchyma overlap for Japanese women.

• 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.

• 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.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.97-102
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• 2016

Deep learning enables machines to have perception and can potentially outperform humans in the medical field. It can save a lot of time and reduce human error by detecting certain patterns from medical images without being trained. The main goal of this paper is to build the optimal model for breast mass classification by applying various methods that influence the performance of Convolutional Neural Network (CNN). Google's newly developed software library Tensorflow was used to build CNN and the mammogram dataset used in this study was obtained from 340 breast cancer cases. The best classification performance we achieved was an accuracy of 0.887, sensitivity of 0.903, and specificity of 0.869 for normal tissue versus malignant mass classification with augmented data, more convolutional filters, and ADAM optimizer. A limitation of this method, however, was that it only considered malignant masses which are relatively easier to classify than benign masses. Therefore, further studies are required in order to properly classify any given data for medical uses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4578-4589
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• 2011

The technologies of image acquisition, display, and storage of the breast have been developed in their specialized fields in recent years. The image acquisition system is a device that absorbs and stores images after examining breast tissue using X-ray. Due to the specificity and sensitivity of imaging, the early detection of breast cancer is feasible. In this paper, the current technologies for digital mammography are reviewed, and we propose a digital mammography and evaluate the performance of the implemented system.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.58-68
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• 2019

Objective: To compare digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) in the detectability of breast cancers in patients with dense breast tissue, and to determine the influencing factors in the detection of breast cancers using the two techniques. Materials and Methods: Three blinded radiologists independently graded cancer detectability of 300 breast cancers (288 women with dense breasts) on DBT and conventional FFDM images, retrospectively. Hormone status, histologic grade, T stage, and breast cancer subtype were recorded to identify factors affecting cancer detectability. The Wilcoxon signed-rank test was used to compare cancer detectability by DBT and conventional FFDM. Fisher's exact tests were used to determine differences in cancer characteristics between detectability groups. Kruskal-Wallis tests were used to determine whether the detectability score differed according to cancer characteristics. Results: Forty breast cancers (13.3%) were detectable only with DBT; 191 (63.7%) breast cancers were detected with both FFDM and DBT, and 69 (23%) were not detected with either. Cancer detectability scores were significantly higher for DBT than for conventional FFDM (median score, 6; range, 0-6; p < 0.001). The DBT-only cancer group had more invasive lobular-type breast cancers (22.5%) than the other two groups (i.e., cancer detected on both types of image [both-detected group], 5.2%; cancer not detected on either type of image [both-non-detected group], 7.3%), and less detectability of ductal carcinoma in situ (5% vs. 16.8% [both-detected group] vs. 27.5% [both-non-detected group]). Low-grade cancers were more often detected in the DBT-only group than in the both-detected group (22.5% vs. 10%, p = 0.026). Human epidermal growth factor receptor-2 (HER-2)-negative cancers were more often detected in the DBT-only group than in the both-detected group (92.3% vs. 70.5%, p = 0.004). Cancers surrounded by mostly glandular tissue were detected less often in the DBT only group than in the both-non-detected group (10% vs. 31.9%, p = 0.016). DBT cancer detectability scores were significantly associated with cancer type (p = 0.012), histologic grade (p = 0.013), T and N stage (p = 0.001, p = 0.024), proportion of glandular tissue surrounding lesions (p = 0.013), and lesion type (p < 0.001). Conclusion: Invasive lobular, low-grade, or HER-2-negative cancer is more detectable with DBT than with conventional FFDM in patients with dense breasts, but cancers surrounded by mostly glandular tissue might be missed with both techniques.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.470-478
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• 2000

Color images from various application areas have their own characteristics. Practical segmentation systems need specialized methods to death with the characteristics. In this paper. we propose a distributed genetic algorithm based segmentation method for color breast carcinoma cell images. To extract positive nuclei and negative nuclei from the cell images, a distributed genetic algorithm with improved genetic operations and an evaluation function is used. As initial values, representative colors from images are introduced to work well with the cell images. A test to verify the validity of the proposed method shows well-segmented images. This result suggests that the method is pertinent to be but into practical use for the images haying limited objects with limited colors.

• Women's Health Nursing
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• v.24 no.2
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• pp.163-173
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• 2018

Purpose: To identify factors influencing psychosocial adjustment in patients with surgical removal of benign breast tumor. Methods: With a survey design, data were collected using the Psychosocial Adjustment to Illness Scale-Self Report (PAIS-SR), Body Image Scale, Physical Discomfort Scale, and Family Support Scale with patients who had had surgical removal of a benign breast tumor from September to November 2017. Data were analysed with descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and stepwise multiple regression. Results: The mean scores for physical discomfort, body image, family support, and psychosocial adjustment were $1.57{\pm}0.51$, $0.37{\pm}0.64$, $3.62{\pm}0.67$, and $4.00{\pm}0.45$, respectively. Family support, body image, physical discomfort, number of surgical removal of benign breast tumor (twice), and cancer insurance status (yes) were verified as factors influencing psychosocial adjustment. These factors accounted for 57.4% of psychosocial adjustment. Conclusion: In this study, family support, body image, and physical discomfort were identified as significant predictors of psychosocial adjustment. Therefore, this study can be used as fundamental data to develop nursing intervention strategies in order to increase psychosocial adjustment in patients with surgical removal of a benign breast tumor.