Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence
BackgroundMammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that ind...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1255109/full |
| _version_ | 1797276825955598336 |
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| author | Chiharu Kai Chiharu Kai Tsunehiro Otsuka Miyako Nara Satoshi Kondo Hitoshi Futamura Naoki Kodama Satoshi Kasai |
| author_facet | Chiharu Kai Chiharu Kai Tsunehiro Otsuka Miyako Nara Satoshi Kondo Hitoshi Futamura Naoki Kodama Satoshi Kasai |
| author_sort | Chiharu Kai |
| collection | DOAJ |
| description | BackgroundMammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT).MethodsWe used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles.ResultsThe average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40–49, 50–59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40–49, 50–59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40–59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36.ConclusionThe study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40–59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles. |
| first_indexed | 2024-03-07T15:34:32Z |
| format | Article |
| id | doaj.art-9e83915ddbb94fab9ab34bb9e18d1d95 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-07T15:34:32Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-9e83915ddbb94fab9ab34bb9e18d1d952024-03-05T11:52:45ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-03-011410.3389/fonc.2024.12551091255109Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligenceChiharu Kai0Chiharu Kai1Tsunehiro Otsuka2Miyako Nara3Satoshi Kondo4Hitoshi Futamura5Naoki Kodama6Satoshi Kasai7Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, JapanMajor in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Niigata, JapanOtsuka Breastcare Clinic, Tokyo, JapanDepartment of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Disease Center, Komagome Hospital, Tokyo, JapanGraduate School of Engineering, Muroran Institute of Technology, Muroran, Hokkaido, JapanHealthcare Business Headquarters, Konica Minolta, Inc., Tokyo, JapanDepartment of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, JapanDepartment of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, JapanBackgroundMammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT).MethodsWe used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles.ResultsThe average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40–49, 50–59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40–49, 50–59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40–59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36.ConclusionThe study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40–59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.https://www.frontiersin.org/articles/10.3389/fonc.2024.1255109/fullmammogrammammary gland content ratiobreast cancerartificial intelligencenon-visible |
| spellingShingle | Chiharu Kai Chiharu Kai Tsunehiro Otsuka Miyako Nara Satoshi Kondo Hitoshi Futamura Naoki Kodama Satoshi Kasai Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence Frontiers in Oncology mammogram mammary gland content ratio breast cancer artificial intelligence non-visible |
| title | Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| title_full | Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| title_fullStr | Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| title_full_unstemmed | Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| title_short | Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| title_sort | identifying factors that indicate the possibility of non visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence |
| topic | mammogram mammary gland content ratio breast cancer artificial intelligence non-visible |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1255109/full |
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