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Edmund E, Kamuzora M, Muhogora W, Ngoya P, Muhulo A, Amirali A, Makoba A, Ngoye W, Ngaile J, Majatta S, Ngulimi M, Mwambinga S, Kaijage T. Radiation dose to breast during digital mammography in Tanzania. RADIATION PROTECTION DOSIMETRY 2024; 200:409-416. [PMID: 38196028 DOI: 10.1093/rpd/ncad316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 11/07/2023] [Accepted: 12/07/2023] [Indexed: 01/11/2024]
Abstract
The aim of this study was to evaluate the mean glandular dose (MGD), to assess the potential for optimization, and to propose diagnostic reference levels (DRLs). MGD was estimated from air kerma measurements and patient information collected during mammography examinations. The 75th percentile values were determined as the third quartile of the median MGD values for all hospitals, and DRLs set as 75th percentile of MGD values. The estimated median values of MGD ranged from 1.5 to 3.9 mGy for craniocaudal projection for median range of 15-59 mm compressed breast thickness (CBT). For a CBT range of 15-63 mm, the median MGD value was 1.5-5.1 mGy for medio-lateral oblique projection. Comparison with other studies showed that the MGD values obtained in this study were relatively high. The magnitude and wide variation of the exposure parameters suggest existing potential for optimization. The training of radiology staff was identified as a top priority.
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Affiliation(s)
- Elisha Edmund
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Mary Kamuzora
- Muhimbili National Hospital, Mloganzila, Kibamba, 16110 Dar es Salaam, Tanzania
| | - Wilbroad Muhogora
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Patrick Ngoya
- Bugando Medical Centre, Makongoro Road, 33830 Mwanza, Tanzania
| | - Alex Muhulo
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Assad Amirali
- Aga Khan Medical Centre, Baraka Obama Drive, 11101 Dar es Salaam, Tanzania
| | - Atumaini Makoba
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Wilson Ngoye
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Justin Ngaile
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Salma Majatta
- Muhimbili National Hospital, Mloganzila, Kibamba, 16110 Dar es Salaam, Tanzania
| | - Miguta Ngulimi
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Salome Mwambinga
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
| | - Tunu Kaijage
- Tanzania Atomic Energy Commission, Block J, Njiro, 23114 Arusha, Tanzania
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Shankari N, Kudva V, Hegde RB. Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review. Crit Rev Biomed Eng 2024; 52:41-60. [PMID: 38780105 DOI: 10.1615/critrevbiomedeng.2024051166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.
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Affiliation(s)
- N Shankari
- NITTE (Deemed to be University), Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte 574110, Karnataka, India
| | - Vidya Kudva
- School of Information Sciences, Manipal Academy of Higher Education, Manipal, India -576104; Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, India - 574110
| | - Roopa B Hegde
- NITTE (Deemed to be University), Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte - 574110, Karnataka, India
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Abdulwahid Noor K, Mohd Norsuddin N, Abdul Karim MK, Che Isa IN, Alshamsi W. Estimating Local Diagnostic Reference Levels for Mammography in Dubai. Diagnostics (Basel) 2023; 14:8. [PMID: 38201317 PMCID: PMC10804395 DOI: 10.3390/diagnostics14010008] [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: 09/26/2023] [Revised: 10/16/2023] [Accepted: 11/02/2023] [Indexed: 01/12/2024] Open
Abstract
As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels (DRLs) for mammography procedures in Dubai at different ranges of breast thickness. A total of 2599 anonymized mammograms were randomly retrieved from a central dose survey database. Mammographic cases for screening women aged from 40 to 69 years were included, while cases of breast implants and breast thickness outside the range of 20-100 mm were excluded. Mean, median, and 75 percentiles were obtained for the mean glandular dose (MGD) distribution of each mammography projection for all compressed breast thickness (CBT) ranges. The local DRLs for mammography in Dubai were found to be between 0.80 mGy and 0.82 mGy for the craniocaudal (CC) projection and between 0.89 mGy and 0.971.8 mGy for the mediolateral oblique (MLO) projection. Local DRLs were proposed according to different breast thicknesses, starting from 20 to 100 mm. All groups of CBT showed a slight difference in MGD values, with higher values in MLO views rather than CC views. The local DRLs in this study were lower than some other Middle Eastern countries and lower than the standard reference levels reported by the International Atomic Energy Agency (IAEA) at 3 mGy/view.
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Affiliation(s)
- Kaltham Abdulwahid Noor
- Dubai Health Academic Corporate, Radiology Department, Rashid Hospital, Dubai 00971, United Arab Emirates;
- Center of Diagnostic, Therapeutic and Investigative Studies (CODTIS), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia;
| | - Norhashimah Mohd Norsuddin
- Center of Diagnostic, Therapeutic and Investigative Studies (CODTIS), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia;
| | | | - Iza Nurzawani Che Isa
- Center of Diagnostic, Therapeutic and Investigative Studies (CODTIS), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia;
| | - Wadha Alshamsi
- SEHA, Medical Physics Department, Al Ain Hospital, Abu Dhabi 80050, United Arab Emirates;
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