Automated Assessment of Breast Positioning Quality in Screening Mammography
Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/19/4704 |
| _version_ | 1797480278033170432 |
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| author | Mouna Brahim Kai Westerkamp Louisa Hempel Reiner Lehmann Dirk Hempel Patrick Philipp |
| author_facet | Mouna Brahim Kai Westerkamp Louisa Hempel Reiner Lehmann Dirk Hempel Patrick Philipp |
| author_sort | Mouna Brahim |
| collection | DOAJ |
| description | Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria. |
| first_indexed | 2024-03-09T21:57:45Z |
| format | Article |
| id | doaj.art-78feafeb68b64996bb55d2e47570232e |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T21:57:45Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-78feafeb68b64996bb55d2e47570232e2023-11-23T19:55:13ZengMDPI AGCancers2072-66942022-09-011419470410.3390/cancers14194704Automated Assessment of Breast Positioning Quality in Screening MammographyMouna Brahim0Kai Westerkamp1Louisa Hempel2Reiner Lehmann3Dirk Hempel4Patrick Philipp5Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhofer Center for Machine Learning, 76131 Karlsruhe, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhofer Center for Machine Learning, 76131 Karlsruhe, GermanyMedical School, Sigmund Freud University, 1090 Vienna, AustriaDontBePatient Intelligence GmbH, 20095 Hamburg, GermanyInstitute of Translational Molecular Tumor Research, 85354 Freising, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhofer Center for Machine Learning, 76131 Karlsruhe, GermanyScreening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria.https://www.mdpi.com/2072-6694/14/19/4704decision supportmammogrampositioning qualitybreast cancercranio-codalmediolateral oblique |
| spellingShingle | Mouna Brahim Kai Westerkamp Louisa Hempel Reiner Lehmann Dirk Hempel Patrick Philipp Automated Assessment of Breast Positioning Quality in Screening Mammography Cancers decision support mammogram positioning quality breast cancer cranio-codal mediolateral oblique |
| title | Automated Assessment of Breast Positioning Quality in Screening Mammography |
| title_full | Automated Assessment of Breast Positioning Quality in Screening Mammography |
| title_fullStr | Automated Assessment of Breast Positioning Quality in Screening Mammography |
| title_full_unstemmed | Automated Assessment of Breast Positioning Quality in Screening Mammography |
| title_short | Automated Assessment of Breast Positioning Quality in Screening Mammography |
| title_sort | automated assessment of breast positioning quality in screening mammography |
| topic | decision support mammogram positioning quality breast cancer cranio-codal mediolateral oblique |
| url | https://www.mdpi.com/2072-6694/14/19/4704 |
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