Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy
This study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery b...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-1729/14/11/1449 |
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| author | Ga Eun Park Bong Joo Kang Sung Hun Kim Han Song Mun |
| author_facet | Ga Eun Park Bong Joo Kang Sung Hun Kim Han Song Mun |
| author_sort | Ga Eun Park |
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| description | This study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery between January 2020 and December 2022 was performed. Pre- and post-NAC mammograms were analyzed using conventional CAD and AI-CAD systems, with negative exams defined by the absence of marked abnormalities. Two radiologists reviewed mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI). Concordance rates between CAD and AI-CAD were calculated, and the diagnostic performance, including the area under the receiver operating characteristics curve (AUC), was assessed. The pre-NAC concordance rates were 90.9% for CAD and 97% for AI-CAD, while post-NAC rates were 88.6% for CAD and 89.4% for AI-CAD. The MRI had the highest diagnostic performance for pCR prediction, with AI-CAD performing comparably to other modalities. Univariate analysis identified significant predictors of pCR, including AI-CAD, mammography, ultrasound, MRI, histologic grade, ER, PR, HER2, and Ki-67. In multivariable analysis, negative MRI, histologic grade 3, and HER2 positivity remained significant predictors. In conclusion, this study demonstrates that AI-CAD in digital mammography shows the potential to examine the pCR of breast cancer patients following NAC. |
| format | Article |
| id | doaj-art-fd74e5af224a4a8d8f18b13b30e17ec5 |
| institution | DOAJ |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-fd74e5af224a4a8d8f18b13b30e17ec52025-08-20T02:47:59ZengMDPI AGLife2075-17292024-11-011411144910.3390/life14111449Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant ChemotherapyGa Eun Park0Bong Joo Kang1Sung Hun Kim2Han Song Mun3Department of Radiology, Seoul Saint Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Radiology, Seoul Saint Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Radiology, Seoul Saint Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Radiology, Seoul Saint Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaThis study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery between January 2020 and December 2022 was performed. Pre- and post-NAC mammograms were analyzed using conventional CAD and AI-CAD systems, with negative exams defined by the absence of marked abnormalities. Two radiologists reviewed mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI). Concordance rates between CAD and AI-CAD were calculated, and the diagnostic performance, including the area under the receiver operating characteristics curve (AUC), was assessed. The pre-NAC concordance rates were 90.9% for CAD and 97% for AI-CAD, while post-NAC rates were 88.6% for CAD and 89.4% for AI-CAD. The MRI had the highest diagnostic performance for pCR prediction, with AI-CAD performing comparably to other modalities. Univariate analysis identified significant predictors of pCR, including AI-CAD, mammography, ultrasound, MRI, histologic grade, ER, PR, HER2, and Ki-67. In multivariable analysis, negative MRI, histologic grade 3, and HER2 positivity remained significant predictors. In conclusion, this study demonstrates that AI-CAD in digital mammography shows the potential to examine the pCR of breast cancer patients following NAC.https://www.mdpi.com/2075-1729/14/11/1449breast cancerartificial intelligencecomputer-aided diagnosismammographyneoadjuvant chemotherapy |
| spellingShingle | Ga Eun Park Bong Joo Kang Sung Hun Kim Han Song Mun Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy Life breast cancer artificial intelligence computer-aided diagnosis mammography neoadjuvant chemotherapy |
| title | Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy |
| title_full | Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy |
| title_fullStr | Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy |
| title_full_unstemmed | Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy |
| title_short | Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy |
| title_sort | efficacy of mammographic artificial intelligence based computer aided detection in predicting pathologic complete response to neoadjuvant chemotherapy |
| topic | breast cancer artificial intelligence computer-aided diagnosis mammography neoadjuvant chemotherapy |
| url | https://www.mdpi.com/2075-1729/14/11/1449 |
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