560-06-010 ISMRM Abstract

Assessing the Effect of ROI Range on Machine Learning Models Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy

Primary: Analysis Methods - Radiomics

Secondary: Body - Breast

560-06-010 · AI Image Processing · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row A

Accepted

Weipeng Zhang ¹, Mengzhou Sun², Xiang Li¹, Xiaoyun Liang³

¹Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China

²Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Beijing, China

³Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China

Presenting Author: Weipeng Zhang

Synopsis

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References

1. Gradishar WJ, Moran MS, Abraham J, et al. Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2024;22(5):331-357. doi:10.6004/jnccn.2024.0035 [doi]

2. Zhuang X, Chen C, Liu Z, et al. Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy. Transl Oncol. 2020;13(11):100831. doi:10.1016/j.tranon.2020.100831 [doi]

3. Daimiel Naranjo, Isaac, et al. "Radiomics and machine learning with multiparametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis." Diagnostics 11.6 (2021): 919.DOI: 10.3390/diagnostics11060919 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-06-010