562-02-005 ISMRM Abstract

Integrating BI-RADS, Radiomics, and Deep Learning for MRI-Based HER2 Phenotype Differentiation

Primary: Body - Breast

Secondary: Analysis Methods - Classification and Prediction

562-02-005 · Breast MRI and the Role of AI · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row C

Keywords: Radiomics Breast MRI HER2 Subtypes Artificial Intelligence (AI) Deep Learning Breast Imaging Reporting and Data System(BI-RADS)

Accepted

Thais Bezerra ¹, Soraia Q Damião², Claudio F Patrocínio², Adriana P Bueno², Marina De Brot², Almir G Bitencourt²

¹University of Campinas, Campinas, Brazil

²A C Camargo Cancer Center, Sao Paolo, Brazil

Presenting Author: Thais Bezerra

Synopsis

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References

1. Ghoncheh M, Pournamdar Z, Salehiniya H. Incidence, mortality, and epidemiology of breast cancer in the world. Asian Pac J Cancer Prev. 2016;17(S3):43-46. doi:10.7314/APJCP.2016.17.S3.43 [doi]

2. Hagemann IS. Molecular testing in breast cancer: a guide to current practices. Arch Pathol Lab Med. 2016;140(8):815-824. doi:10.5858/arpa.2016-0051-RA [doi]

3. Schettini F, Pascual T, Conte B, et al. Trastuzumab deruxtecan in HER2-low metastatic breast cancer. N Engl J Med. 2022;387(1):9-20. doi:10.1056/NEJMoa2203690 [doi]

4. Cong C, Li X, Zhang C, Zhang J, Sun K, Liu L, Ambale-Venkatesh B, Chen X, Wang Y. MRI-based breast cancer classification and localization by multiparametric feature extraction and combination using deep learning. J Magn Reson Imaging. 2024;59(1):148-161. doi:10.1002/jmri.28713 [doi]

5. Le EPV, Wang Y, Huang Y, Hickman S, et al. Artificial intelligence in breast imaging. Clin Radiol. 2019;74(5):357-366. doi:10.1016/j.crad.2019.02.006 [doi]

6. Morris EA, Comstock CE, Lee CH, et al. ACR BI-RADS® Magnetic Resonance Imaging. In: ACR BI-RADS® Atlas: Breast Imaging Reporting and Data System. 5th ed. Reston, VA: American College of Radiology; 2013.

7. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104-e107. doi:10.1158/0008-5472.CAN-17-0339 [doi]

8. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint. arXiv:1409.1556. 2014. https://arxiv.org/abs/1409.1556

9. Allison KH, Hammond MEH, Dowsett M, McKernin SE, et al. Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update. J Clin Oncol. 2020;38(12):1346-1366. doi:10.1200/JCO.19.02309 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-02-005