1
|
Breast Cancer in Asia: Incidence, Mortality, Early Detection, Mammography Programs, and Risk-Based Screening Initiatives. Cancers (Basel) 2022; 14:cancers14174218. [PMID: 36077752 PMCID: PMC9454998 DOI: 10.3390/cancers14174218] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 12/09/2022] Open
Abstract
Simple Summary Nearly all breast cancer patients survive for more than five years when the tumor is found early and in the localized stage. Regular clinical breast examinations, mammograms, and monthly self-exams of the breasts all contribute to early detection. However, late-stage breast cancers are common in many Asian countries. Low-income countries suffer from a lack of resources for breast cancer screening. High-income countries, on the other hand, are not benefiting fully from national breast screening programs due to an underutilization of the preventive healthcare services available. Existing reviews on Asian breast cancers are heavily focused on risk factors. The question of whether we should adopt or adapt the knowledge generated from non-Asian breast cancers would benefit from an extension into screening guidelines. In addition, several Asian countries are piloting studies that move away from the age-based screening paradigm. Abstract Close to half (45.4%) of the 2.3 million breast cancers (BC) diagnosed in 2020 were from Asia. While the burden of breast cancer has been examined at the level of broad geographic regions, literature on more in-depth coverage of the individual countries and subregions of the Asian continent is lacking. This narrative review examines the breast cancer burden in 47 Asian countries. Breast cancer screening guidelines and risk-based screening initiatives are discussed.
Collapse
|
2
|
Yamamuro M, Asai Y, Hashimoto N, Yasuda N, Kimura H, Yamada T, Nemoto M, Kimura Y, Handa H, Yoshida H, Abe K, Tada M, Habe H, Nagaoka T, Nin S, Ishii K, Kondo Y. Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms. Biomed Phys Eng Express 2022; 8. [PMID: 35728581 DOI: 10.1088/2057-1976/ac7ada] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022]
Abstract
This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the 'dense region'); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the 'diffuse region'). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland-Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland-Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were -0.0299 and -0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.
Collapse
Affiliation(s)
- Mika Yamamuro
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan.,Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
| | - Yoshiyuki Asai
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Naomi Hashimoto
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Nao Yasuda
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Hiorto Kimura
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography Institute of Advanced Clinical Medicine, Kindai University, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Mitsutaka Nemoto
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Yuichi Kimura
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Hisashi Handa
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hisashi Yoshida
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Koji Abe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Masahiro Tada
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hitoshi Habe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Takashi Nagaoka
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Seiun Nin
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Kazunari Ishii
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Yohan Kondo
- Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
| |
Collapse
|
3
|
Ho PJ, Wong FY, Chay WY, Lim EH, Lim ZL, Chia KS, Hartman M, Li J. Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore. Cancer Med 2021; 10:8182-8191. [PMID: 34708579 PMCID: PMC8607242 DOI: 10.1002/cam4.4297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/10/2021] [Accepted: 08/26/2021] [Indexed: 12/19/2022] Open
Abstract
Background Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density). Methods In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5‐year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified. Results Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8–3.1], adjusted HR: 1.9 [1.4–2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559–0.670], adjusted AUC: 0.703 [0.653–0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5‐year absolute risk [absolute risk ≥1.43%], were recalled, and/or mammographic density ≥50%). Conclusion The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.
Collapse
Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Wen Yee Chay
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Zi Lin Lim
- Genome Institute of Singapore, Singapore, Singapore
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Genome Institute of Singapore, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore
| |
Collapse
|