Digital mammography with AI-based computer-aided diagnosis to predict neoadjuvant chemotherapy response in HER2-positive and triple-negative breast cancer patients: comparison with MRI.
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| Title: | Digital mammography with AI-based computer-aided diagnosis to predict neoadjuvant chemotherapy response in HER2-positive and triple-negative breast cancer patients: comparison with MRI. |
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| Authors: | Kim H; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Choi JS; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. jisoo.choi@samsung.com.; Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea. jisoo.choi@samsung.com., Chi SA; Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea., Ryu JM; Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Lee JE; Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Kim MK; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Lee J; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Ko ES; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Ko EY; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Han BK; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. |
| Source: | European radiology [Eur Radiol] 2025 Sep; Vol. 35 (9), pp. 5671-5684. Date of Electronic Publication: 2025 Mar 25. |
| Publication Type: | Journal Article; Comparative Study |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1432-1084 |
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| DOI: | 10.1007/s00330-025-11390-x |
