Effect of Digital Mammography for Breast Cancer Screening: A                    Comparative Study of More than 8 Million Korean Women

Hong, Seri; Song, Soo Yeon; Park, Boyoung; Choi, Kui Son; Jung, Seung Eun; Kim, Min Jung; Lee, Eun Hye; Lee, Chan Wha; Jun, Jae Kwan

doi:10.1148/radiol.2019190951

Cited by 35 publications

(20 citation statements)

References 31 publications

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“…We assumed the cancer prevalence at 3.21 per 1,000 examinations [ 15 ]. With projection of the assumed cancer prevalence to the previous results, the target sample size was chosen as 32,714 participants, corresponding to approximately 16,000 participants per year.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Chang

Choi

et al. 2022

J Breast Cancer

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Purpose Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists’ performance and provides results equivalent or superior to those of radiologists’ alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk. Methods Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening. Discussion The Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase. Trial Registration ClinicalTrials.gov Identifier: NCT05024591

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Chang

Choi

et al. 2022

J Breast Cancer

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“…In particular, in the case of patients under 50 years of age, the sensitivity of SFM was 49.0% to 53.7% and the specificity was 86.0% to 86.2%. In the case of FFDM, the sensitivity in women under the age of 50 was 54.6% to 54.5% and the specificity was 85.3% to 86.0%, which was better than SFM, but the sensitivity was still low [21]. These results show the importance of developing a new auxiliary diagnostic method that can improve the sensitivity of mammography and are considered important reference material for evaluating the significance of the results of this study.…”

Section: Discussionmentioning

confidence: 64%

Evaluation of the Value of Multiplex MicroRNA Analysis as a Breast Cancer Screening in Korean Women under 50 Years of Age with a High Proportion of Dense Breasts

Jang

Jung

et al. 2021

J Cancer Prev

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This study was conducted to confirm the performance of the microRNA (miRNA) biomarker combination as a new breast cancer screening method in Korean women under the age of 50 with a high percentage of dense breasts. To determine the classification performance of a set of miRNA biomarkers (miR-1246, 202, 21, and 219B) useful for breast cancer screening, we determined whether there was a significant difference between the breast cancer and healthy control groups through box plots and the Mann-Whitney U-test, which was further examined in detail by age group. To verify the classification performance of the 4 miRNA biomarker set, 4 classification methods (logistic regression, random forest, XGBoost, and generalized linear model plus random forest) were applied, and 10-fold cross-validation was used as a validation method to improve performance stability. We confirmed that the best breast cancer detection performance was achievable in patients under 50 years of age when the set of 4 miRNAs were used. Under the age of 50, the 4 miRNA biomarkers showed the highest performance with a sensitivity of 85.29%, specificity of 93.33%, and area under the curve (AUC) of 0.961. Examining the results of 4 miRNA biomarkers was found to be an effective strategy for diagnosing breast cancer in Korean women under 50 years of age with dense breasts, and hence has the potential as a new breast cancer screening tool. Further validation in an appropriate screening population with large-scale clinical trials is required.

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“…In developed countries, organized and opportunistic screening programs have significantly reduced breast cancer mortality. Although two-dimensional mammography uses a new detector, it is well known that it still has its limitations because the normal structures and pathological structures may overlap each other when obtaining the transmission X-ray image [3].…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach

Yang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In this work, a computer-aided architecture for mass regions segmentation in DBT images using a dilated deep convolutional neural network (DCNN) is developed. First, to improve the low contrast of breast tumour candidate regions and depress the background tissue noise in the DBT image effectively, the constraint matrix is established after top-hat transformation and multiplied with the DBT image. Second, input image patches are generated, and the data augmentation technique is performed to create the training data set for training a dilated DCNN architecture. Then the mass regions in DBT images are preliminarily segmented; each pixel is divided into two different kinds of labels. Finally, the postprocessing procedure removes all false-positives regions with less than 50 voxels. The final segmentation results are obtained by smoothing the boundaries of the mass regions with a median filter. The testing accuracy (ACC), sensitivity (SEN), and the area under the receiver operating curve (AUC) are adopted as the evaluation metrics, and the ACC, SEN, as well as AUC are 86.3%, 85.6%, and 0.852 for segmenting the mass regions in DBT images on the entire data set, respectively. The experimental results indicate that our proposed approach achieves promising results compared with other classical CAD-based frameworks.

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Effect of Digital Mammography for Breast Cancer Screening: A Comparative Study of More than 8 Million Korean Women

Cited by 35 publications

References 31 publications

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Evaluation of the Value of Multiplex MicroRNA Analysis as a Breast Cancer Screening in Korean Women under 50 Years of Age with a High Proportion of Dense Breasts

Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach

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