Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms
Purpose In this retrospective study, we aimed to assess the efficacy of artificial intelligencebased computer-aided detection (AI-CAD) for breast cancer detection on mammograms. Materials and Methods Mammograms from 269 women with breast cancer were analyzed. Cancer visibility was determined base...
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| Format: | Article |
| Language: | English |
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The Korean Society of Radiology
2025-05-01
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| Series: | Journal of the Korean Society of Radiology |
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| Online Access: | https://doi.org/10.3348/jksr.2024.0061 |
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| author | Sunhee Bien Ga Eun Park Bong Joo Kang Sung Hun Kim |
| author_facet | Sunhee Bien Ga Eun Park Bong Joo Kang Sung Hun Kim |
| author_sort | Sunhee Bien |
| collection | DOAJ |
| description | Purpose In this retrospective study, we aimed to assess the efficacy of artificial intelligencebased
computer-aided detection (AI-CAD) for breast cancer detection on mammograms.
Materials and Methods Mammograms from 269 women with breast cancer were analyzed.
Cancer visibility was determined based on reports from experienced radiologists. Two expert
radiologists assessed mammographic findings and breast imaging reporting and data
system (BI-RADS) categories by consensus for cases of visible cancer. AI-CAD results were
reviewed to determine whether AI-CAD correctly marked the cancer site. AI-CAD detection
rates were analyzed according to mammographic findings, BI-RADS categories, lesion size,
histologic grade, lymph node involvement, and stage. The concordance between the findings
of AI-CAD and those of experienced radiologists was also assessed. Mammographically occult
cases were defined as those with negative mammographic findings by two radiologists.
Results AI-CAD detected 81.4% (219/269) of cancers, with higher detection rates occurring
for larger lesion sizes, high histologic grades, lymph node involvement, and advanced stages.
AI-CAD detection rates were higher for architectural distortion, mass, and calcification,
but lower for asymmetry. Detection rates increased with higher BI-RADS categories and a
higher number of mammography findings. Concordance between the assessment of AICAD
and that of experienced radiologists was 88.5% (238/269). AI-CAD correctly detected
19.4% (6/31) of mammographically occult cases.
Conclusion AI-CAD detected 81.4% of cancers, with substantial concordance with the findings
of experienced radiologists. It correctly identified 19.4% of mammographically occult cases. |
| format | Article |
| id | doaj-art-2abcdb0ea6f34e07adf38256da2d1bcc |
| institution | OA Journals |
| issn | 2951-0805 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | The Korean Society of Radiology |
| record_format | Article |
| series | Journal of the Korean Society of Radiology |
| spelling | doaj-art-2abcdb0ea6f34e07adf38256da2d1bcc2025-08-20T02:17:26ZengThe Korean Society of RadiologyJournal of the Korean Society of Radiology2951-08052025-05-01863391406https://doi.org/10.3348/jksr.2024.0061Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital MammogramsSunhee BienGa Eun ParkBong Joo KangSung Hun KimPurpose In this retrospective study, we aimed to assess the efficacy of artificial intelligencebased computer-aided detection (AI-CAD) for breast cancer detection on mammograms. Materials and Methods Mammograms from 269 women with breast cancer were analyzed. Cancer visibility was determined based on reports from experienced radiologists. Two expert radiologists assessed mammographic findings and breast imaging reporting and data system (BI-RADS) categories by consensus for cases of visible cancer. AI-CAD results were reviewed to determine whether AI-CAD correctly marked the cancer site. AI-CAD detection rates were analyzed according to mammographic findings, BI-RADS categories, lesion size, histologic grade, lymph node involvement, and stage. The concordance between the findings of AI-CAD and those of experienced radiologists was also assessed. Mammographically occult cases were defined as those with negative mammographic findings by two radiologists. Results AI-CAD detected 81.4% (219/269) of cancers, with higher detection rates occurring for larger lesion sizes, high histologic grades, lymph node involvement, and advanced stages. AI-CAD detection rates were higher for architectural distortion, mass, and calcification, but lower for asymmetry. Detection rates increased with higher BI-RADS categories and a higher number of mammography findings. Concordance between the assessment of AICAD and that of experienced radiologists was 88.5% (238/269). AI-CAD correctly detected 19.4% (6/31) of mammographically occult cases. Conclusion AI-CAD detected 81.4% of cancers, with substantial concordance with the findings of experienced radiologists. It correctly identified 19.4% of mammographically occult cases.https://doi.org/10.3348/jksr.2024.0061breast neoplasmsmammographyartificial intelligencecomputer-assisted diagnosis |
| spellingShingle | Sunhee Bien Ga Eun Park Bong Joo Kang Sung Hun Kim Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms Journal of the Korean Society of Radiology breast neoplasms mammography artificial intelligence computer-assisted diagnosis |
| title | Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms |
| title_full | Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms |
| title_fullStr | Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms |
| title_full_unstemmed | Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms |
| title_short | Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms |
| title_sort | exploring the efficacy of artificial intelligence based computer aided detection for breast cancer detection on digital mammograms |
| topic | breast neoplasms mammography artificial intelligence computer-assisted diagnosis |
| url | https://doi.org/10.3348/jksr.2024.0061 |
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