• Journal of the Korean Society of Radiology
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• v.6 no.1
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• pp.63-71
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• 2012

This study aimed to evaluate the accuracy of breast cancer diagnosis of digital mammography which is in the highest interest of breast imaging test, and to investigate the characteristics of breast cancer patients. For this purpose, 57 breast cancer patients who underwent breast imaging test were examined between May 2010 and June 2011. The average age of the breast cancer patients was 50.8 years old, and the most frequently occurring location was the upper outer quadrant (UOQ), accounting for 33.3%. By age, the highest occurrence rate of breast cancer was the age group of 40~49, accounting for 42.1%. As for the breast composition of the breast cancer patients, fatty breast accounted for 31.6% (18/57) and dense breast for 68.4% (39/57), indicating that nearly 70% of the breast cancer patients have dense breast. It was found that the detection rate of breast cancer was the highest (45.3%) when both microcalcification and mass are simultaneously present in the radiographic lesion of the breast imaging. In dense breast, the mass without microcalcification was lower in detection rate than fatty breast. Accordingly, the mass is the cause of raising the false negative rate in dense breast. The findings show that the false negative rate of digital mammography was 7.0% and the sensitivity 93.0%. Also, the false negative rate of dense breast was 12.8%, and the sensitivity 87.2%, indicating that the sensitivity to breast cancer in this study was higher than the dense breast of previously reported screen film mammography.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4063-4066
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• 2012

To determine the clinical outcome of breast cancer BI-RADS 4 lesions and seek a more effective management guideline, we conducted a retrospective study of all BI-RADS4 patients diagnosed between 2003-2008 with follow up time not less than 2 years. A total of 392 cases of BI-RADS 4 were identified and 320 could be sub-categorised as 4a, 4b and 4c. Overall malignant positive results were 7.65, 38.7 and 58.percent, respectively. In all cases assigned to the close follow up group, no malignancy was detectable (P<0.02). The results of the study suggested that BI-RADS sub-categories have benefit for cancer diagnosis and treatment decisions of clinicians and it might be possible to set up a safe follow-up guideline in selected groups of patients to minimize un-necessary tissue biopsy for breast cancer detection.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9101-9105
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• 2014

Background: The purpose of this study was to compare the accuracy and effectiveness of automated breast volume scanning (ABVS) and hand-held ultrasound (HHUS) in the detection of breast cancer in a large population group with a long-term follow-up, and to investigate whether different ultrasound systems may influence the estimation of cancer detection. Materials and Methods: Institutional review board approval was obtained for this retrospective study, and informed consent was waived. From September 2010 to August 2011, a total of 1,866 ABVS and 3,700 HHUS participants, who underwent these procedures at our institute, were included in this study. Cancers occurring during the study and subsequent follow-up were evaluated. The reference standard was a combination of histology and follow-up imaging (${\geq}12months$). The recall rate, cancer detection yield, diagnostic accuracy, sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with exact 95% confidence intervals. Results: The recall rate was 2.57 per 1,000 (48/1,866) for ABVS and 3.57 per 1,000 (132/3,700) for HHUS, with a significant difference (p=0.048). The cancer detection yield was 3.8 per 1,000 for ABVS and 2.7 per 1,000 for HHUS. The diagnostic accuracy was 97.7% for ABVS and 96.5% for HHUS with statistical significance (p=0.018). The specificity of ABVS and HHUS were 97.8%, 96.7%, respectively (p=0.022). Conclusions: ABVS shows a comparable diagnostic performance to HHUS. ABVS is an effective supplemental tool for mammography in breast cancer detection in a large population.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3741-3745
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• 2016

Background: This study was carried out to examine breast cancer risk and its fertility predictors in women aged ${\geq}35$. Materials and Methods: This cross-sectional study was conducted on 560 healthy women referred to health centers of Tabriz-Iran, 2013-2014. Five-year and lifetime risk of developing breast cancer were determined using the Gail model. General linear modeling was applied to determine breast cancer predictors. Results: The mean age of the subjects was 42.7 (SD: 7.7) years. Mean 5-year and lifetime risks of developing breast cancer were determined to be 0.6% (SD: 0.2%) and 8.9% (SD: 2.5%), respectively. Variables of family history of breast cancer, age, age at menarche, parity, age at first childbirth, breastfeeding history, frequency of breastfeeding, method of contraception, marital status and education were all found to be predictors of breast cancer risk. Conclusions: According to the results of this study, screening programs based on the Gail model should be implemented for Iranian people who have a high risk for breast cancer in order to facilitate early detection and better plan for possible malignancies.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4919-4923
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• 2013

Breast cancer is the top cancer in women worldwide and its incidence is increasing, particularly in developing countries. In the United Arab Emirates (UAE), many cases are first diagnosed in later stages and at younger age compared to those seen in developed countries. Early detection in order to improve breast cancer outcome and survival remains the cornerstone of breast cancer control. Performance of breast self examination is one of the important steps for identifying breast disease at an early stage, by the woman herself. No information has hitherto been available about the frequency of this practice among female university students in UAE or about their breast cancer risk perception and therefore the present study was conducted in Ajman. It was found that 22.7% of the participants practiced BSE but only 3% of them practiced BSE monthly. Marital status but not age as significantly associated with age likelihood. The most frequent reported barriers for BSE were lack of knowledge, considering oneself not at risk and the absence of doctor advice. These factors need to be taken into account in intervention efforts.

• Journal of Korean Academy of Nursing
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• v.33 no.8
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• pp.1119-1126
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• 2003

Background. Breast cancer is the most common form of cancer among Korean women. Only 14 % of urban women and 10% of rural women in Korea, however, participated in breast cancer screening behavior in 1998 (Korean Ministry of Health & Welfare, 1999). Purpose. The aim of this study was to evaluate the effect of community-based breast self-examination (BSE) education programs in Korea. Methods. First, breast cancer risk appraisals were done with 1,977 rural women. Of the 1,977 women, nearly 30% (n=494) had a higher or equal to borderline risk of developing breast cancer. This quasi-experimental study was conducted to target these women with a high or equal to borderline risk of breast cancer. The risk appraisal feedback and breast self-examination education were used as an intervention for breast cancer prevention and early detection. Results. After a 3-month follow-up, 30.5% of the women in the intervention group performed regular BSE compared to 10.2 % of women in the control group. The mean knowledge score related to breast cancer and BSE was significantly higher for the women in the intervention group than that in the control group.

• Journal of the Korean Society of Radiology
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• v.82 no.1
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• pp.12-28
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• 2021

Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6735-6740
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• 2015

Background: With the recent epidemiologic transition in Thailand, featuring decreasing incidences of infectious diseases along with increasing rates of chronic conditions, cancer is becoming a serious problem for the country. Breast cancer has the highest incidence rates among females, not only in the southern regions, but throughout Thailand. Surat Thani is a province in the upper part of Southern Thailand. A study was needed to identify the current burden, and the future trends of breast cancer. Materials and Methods: Here we used cancer incidence data from the Surat Thani Cancer Registry to characterize the incidences of breast cancer. Joinpoint analysis was used to investigate the incidences in the province from 2004 to 2012 and to project future trends from 2013 to 2030. Results: Age-standardized incidence rates (world) of breast cancer in the upper parts of Southern Thailand increased from 35.1 to 59.2 cases per 100,000 female population, which is equivalent to an annual percentage change of 4.5-4.8%. Linear drift effects played a role in shaping the increase of incidence. Joinpoint projection suggested that incidence rates would continue to increase in the future with incidence for women aged 50 and above, at a higher rate than for women below the age of 50. Conclusions: The current early detection measures increase detection rates of early disease. Preparation of a budget for treatment facilities and human resources, both in surgical and medical oncology, is essential.