• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.127-130
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• 2015

Direct injection of foreign material, such as liquid paraffin and silicone, into the breast can induce a foreign body granulomatous reaction and fibrosis, resulting in hard, nodular breast masses and architectural distortion that can mimic neoplasm. Conventional methods, including physical examination, mammography, and ultrasonography are of little use to differentiate between foreign body-induced mastopathy and breast cancer. In patients with foreign body injection such as breast augmentation, dynamic contrast enhanced MR imaging is an excellent imaging modality. Here, the authors report the MR imaging and pathological findings of ductal carcinoma in situ (DCIS) with multicystic changes in a 41-year-old woman with a previous history of interstitial mammoplasty by paraffin injection.

• Journal of Digital Contents Society
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• v.11 no.2
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• pp.217-224
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• 2010

The purpose of this study was to examine the usefulness of IDEAL technique in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, fat-suppressed T2-weighted images and T1-weighted images before and after contrast enhancement were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: 1) On T2-weighted images, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 2) On T1-weighted images before enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 3) On T1-weighted images after enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.583-587
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• 2013

This study aimed to investigate tumor microvessel density (MVD) and lymphatic vessel density (LVD) using the Chalkley method as predictive markers for the risk of axillary lymph node metastasis and their relationship to other clinicopathological parameters in primary breast cancer cases. Forty two node-positive and eighty node-negative breast cancers were immunostained for CD34 and D2-40. MVD and LVD were counted by the Chalkley method at x400 magnification. There was a positive significant correlation of the MVD with the tumor size, coexisting ductal carcinoma in situ (DCIS) and lymph node metastases (P<0.05). In multivariate analysis, the MVD (2.86-4: OR 5.87 95%CI 1.05-32; >4: OR 20.03 95%CI 3.47-115.55), lymphovascular invasion (OR 3.46, 95% CI 1.13-10.58), and associated DCIS (OR 3.1, 95%CI 1.04-9.23) independently predicted axillary lymph node metastasis. There was no significant relationship between LVD and axillary lymph node metastasis. However, D2-40 was a good lymphatic vessel marker to enhance the detection of lymphatic invasion compared to H and E staining. In conclusion, MVD by the Chalkley method, lymphovascular invasion and associated DCIS can be additional predictive factors for axillary lymph node metastases in breast cancer. No relationship was identified between LVD and clinicopathological variables, including axillary lymph node metastasis.

• 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 Digital Contents Society
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• v.12 no.2
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• pp.157-164
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• 2011

The purpose of this study was to examine the usefulness of 3D reconstruction images in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, subtraction images and 3D reconstruction images were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: The 3D reconstruction images showed higher SNR at the lesion area, ductal area, and fat area that of the subtraction image. In addition, the CNR were not significantly different in the lesion area itself between the subtraction images and 3D reconstruction images.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.357-364
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• 1997

The early detection of breast cancer is clearly a key ingredient for reducing breast cancer mortality. Microcalcification is the only visible feature of the DCIS's(ductal carcinoma in situ) which consist 15 ~ 20% of screening-detected breast cancer. Therefore, the analysis of the shapes and distributions of microcalcifications is very significant for the early detection. The automatic detection procedures have b(:on the concern of digital image processing for many years. We proposed here one efficient method which is essentially statistical pattern classification accelerated by one representative feature, correlation coefficient. We compared the results by this additional feature with results by a simple gray level thresholding. The average detection rate was increased from 48% by gray level feature only to 83% by the proposed method The performances were evaluated with TP rates and FP counts, and also with Bayes errors.

• Wind and Structures
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• v.21 no.5
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• pp.489-503
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• 2015

This paper surveys and complements contributions by the National Institute of Standards and Technology to techniques ensuring that the wind tunnel procedure for the design of high-rise structures is based on sound methods and allows unambiguous inter-laboratory comparisons. Developments that enabled substantial advances in these techniques include: Instrumentation for simultaneously measuring pressures at multiple taps; time-domain analysis methods for estimating directional dynamic effects; creation of large simulated extreme directional wind speed data sets; non-parametric methods for estimating mean recurrence intervals (MRIs) of Demand-to-Capacity Indexes (DCIs); and member sizing based on peak DCIs with specified MRIs. To implement these advances changes are needed in the traditional division of tasks between wind and structural engineers. Wind engineers should provide large sets of directional wind speeds, pressure coefficient time series, and estimates of uncertainties in wind speeds and pressure coefficients. Structural engineers should perform the dynamic analyses, estimates of MRIs of wind effects, sensitivity studies, and iterative sizing of structural members. The procedure is transparent, eliminates guesswork inherent in frequency domain methods and due to the lack of pressure measurements, and enables structural engineers to be in full control of the structural design for wind.

• Investigative Magnetic Resonance Imaging
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• v.17 no.2
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• pp.73-82
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• 2013

Purpose : To determine the quantitative parameters of breast MRI that predict tumor invasion in biopsy-proven DCIS. Materials and Methods: From January 2009 to March 2010, 42 MRI examinations of 41 patients with biopsy-proven DCIS were included. The quantitative parameters, which include the initial percentage enhancement ($E_1$), peak percentage enhancement ($E_{peak}$), time to peak enhancement (TTP), signal enhancement ratio (SER), arterial enhancement fraction (AEF), apparent diffusion coefficient (ADC) value, long diameter and the volume of the lesion, were calculated as parameters that might predict invasion. Univariate and multivariate analyses were used to identify the parameters associated with invasion. Results: Out of 42 lesions, 23 lesions were confirmed to be invasive ductal carcinoma (IDC) and 19 lesions were confirmed to be pure DCIS. Tumor size (p = 0.003; $6.5{\pm}3.2$ cm vs. $3.6{\pm}2.6$ cm, respectively) and SER (p = 0.036; $1.1{\pm}0.3$ vs. $0.9{\pm}0.3$, respectively) showed statistically significant high in IDC. In contrast, E1, Epeak, TTP, ADC, AEF and volume of the lesion were not statistically significant. Tumor size and SER had statistically significant associations with invasion, with an odds ratio of 1.04 and 22.93, respectively. Conclusion: Of quantitative parameters analyzed, SER and the long diameter of the lesion could be specific parameter for predicting invasion in the biopsy-proven DCIS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.454-458
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• 2017

Fibroadenoma are one of the most common benign tumors of the breast in young women. Fibroadenoma may be associated with fibrocystic change, proliferative epithelial changes, and extremely rarely with carcinoma. We report here two cases of malignancy arising from a breast fibroadenoma. The patients were 19 and 51 years old and presented with a lump of recent onset. A 19-year-old female patient was diagnosed with mass excision at another hospital, and re-excision was performed at the hospital. Ultrasonography and cytologic examination revealed fibroadenoma and malignancy in a 51-year-old female patient, who was treated with wide excision. The pathological report of the two cases was revealed as DCIS in a fibroadenoma. Because carcinoma arising within a fibroadenoma is so rare, there are few reports of its characteristics or guidelines for treatment. Careful analysis of the unusual carcinoma arising within a fibroadenoma of the breast led to appropriate diagnosis and treatment of various types of lesions. Herein, we report two cases of DCIS arising within a fibroadenoma of the breast and provide a review of the literature.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.22-30
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• 2001

In this paper we propose three classification algorithms to classify breast tumors that occur in duct into Benign, DCIS(ductal carcinoma in situ) NOS(invasive ductal carcinoma) The general approach for a creating classifier is composed of 2 steps: feature extraction and classification Above all feature extraction for a good classifier is very significance, because the classification performance depends on the extracted features, Therefore in the feature extraction step, we extracted morphology features describing the size of nuclei and texture features The internal structures of the tumor are reflected from wavelet transformed images with 10$\times$ and 40$\times$ magnification. Pariticulary to find the correlation between correct classification rates and wavelet depths we applied 1, 2, 3 and 4-level wavelet transforms to the images and extracted texture feature from the transformed images The morphology features used are area, perimeter, width of X axis width of Y axis and circularity The texture features used are entropy energy contrast and homogeneity. In the classification step, we created three classifiers from each of extracted features using discriminant analysis The first classifier was made by morphology features. The second and the third classifiers were made by texture features of wavelet transformed images with 10$\times$ and 40$\times$ magnification. Finally we analyzed and compared the correct classification rate of the three classifiers. In this study, we found that the best classifier was made by texture features of 3-level wavelet transformed images.