ISMRM25 - Advances in Breast Cancer Imaging

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

ISMRM & ISMRT 2025 Annual Meeting & Exhibition

Digital Poster

Advances in Breast Cancer Imaging

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Advances in Breast Cancer Imaging

Digital Poster

Body

Monday, 12 May 2025

Exhibition Hall

16:00 - 17:00

Session Number: D-70

No CME/CE Credit

		Computer Number: 33 1990. MRE-derived Tumor Stiffness as a Non-invasive Technique to Characterize Breast Tumors View Presentation Video A. Deavela, B. Griffith, J. Hawley, K. Thompson, A. Kolipaka The Ohio State University, Columbus, United States Impact: The statistically significant consistency between the non-invasive MRE-derived tumor stiffness and the current gold-standards of diagnosis, histological grading and DCE-MRI, means that breast tumor characterization through MRE is possible while maintaining sensitivity and specificity.
		Computer Number: 34 1991. Evaluating Large Language Model’s Potential to Optimize Structured MRI Reporting in Breast Cancer Diagnosis View Presentation Video Y. Song, M. He, A. Wang, C. Wang, G. Yang MR Research Collaboration Team, Siemens Healthineers Ltd, Shanghai, China Impact: This study highlights the promising role of AI in radiology, potentially enhancing diagnostic accuracy and supplementing radiological expertise, especially in multilingual and resource-limited settings.
		Computer Number: 35 1992. The predictive value of preoperative Multiparametric MRI Radiomics Model for axillary lymph node metastasis in breast cancer Q. Xinyu, W. Wenjia, G. Lihong Affiliated Hospital of Mongolia Medical University, Hohhot, China Impact: These radiomics models serve as a noninvasive tool to predict ALN metastasis in BRCA preoperatively.
		Computer Number: 36 1993. Predictive value of DCE-MRI perfusion parameters for pathological grading of invasive breast cancer in dense breasts Y. Ou, L. Huang, B. Liu Guiqian International General Hospital, Gui zhou, China Impact: Preoperative prediction of breast cancer pathological grading in dense breasts aids in optimizing patient treatment decisions.Our results indicate that these parameters can be used preoperatively to predict the pathological grading of breast cancer in dense breasts.
		Computer Number: 37 1994. Improved Breast Cancer Characterization with High-Resolution 3D T2WI-SPACE at Equivalent Scan Times to Conventional 2D FSE-T2WI View Presentation Video K. Ichikawa, H. Satake, M. Iima, S. Ishigaki, Y. Kato, S. Naganawa Department of Radiological Technology, Nagoya University Hospital, Nagoya, Japan Impact: Our findings strongly support the feasibility of high-resolution 3D T2WI for implementation in clinical settings, offering valuable information to contrast-enhanced MRI for breast cancer characterization. This technique would show promise for improving lesion differentiation, prognosis prediction, and treatment efficacy assessment.
		Computer Number: 38 1995. Deep Learning Reconstruction for Accelerating Synthetic MRI in Breast Imaging: A Comparative Analysis of Accelerated and Standard Protocols F. Yang, Y. Jiang, Y. Xiao, J. Sun, B. Zhang, H. Liang West China Hospital of Sichuan University, Chengdu, China Impact: Deep learning reconstruction for accelerating synthetic MRI is poised to enhance the clinical application of synthetic MRI in diagnosing breast diseases, thereby improving examination efficiency.
		Computer Number: 39 1996. The improvement of T2 weighted and diffusion weighted image quality in breast magnetic resonance imaging by deep learning reconstruction s. z. Yang, y. Ji, z. t. Yu, n. Yao, J. Guo, H. LU Tianjin Medical University Cancer Institute and Hospital, Tianjin, China Impact: This evaluation can be useful for reasonable T2WI and DWI breast imaging protocol picking based on deep learning methods, which may reducethepatients’ uncomfortable or help better lesion characterization.
		Computer Number: 40 1997. Super-Resolution DLR (i.e. PIQE) and Expanded SPEEDER on Breast MRI: Image Quality Improvement without Any Influence on Contrast and ADC View Presentation Video D. Takenaka, T. Ueda, K. Yamamoto, Y. Sano, M. Ikedo, M. Ozaki, M. Yui, H. Nagata, M. Nomura, T. Yoshikawa, Y. Ozawa, Y. Ohno Fujita Health University School of Medicine, Toyoake, Japan Impact: Super-resolution DLR (i.e. PIQE) and Expanded SPEEDER had superior potential for image quality and spatial resolution improvements without any influence on contrast and ADC evaluation on breast MRI, when compared with conventional parallel imaging (SPEEDER) and reconstruction method.
		Computer Number: 41 1998. MRI-based radiomics model for predicting pathologic response to neoadjuvant therapy in HER2-positive breast cancer J. Zhang Shanxi Province Cancer Hospital, Taiyuan, China Impact: MRI-based radiomics models may serve as a noninvasive predictor of pathological complete response. Accurate prediction of pCR is of great significance for timely termination of neoadjuvant therapy to avoid toxic and side-effects, and even for future exemption of breast surgery.
		Computer Number: 42 1999. Combination of time‑dependent diffusion MRI and Intravoxel Incoherent Motion for Predicting NPI and Molecular Subtypes of Breast Cancer L. He, Z. Liu, T. Ai Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Impact: The integration of td-dMRI and IVIM provides a new perspective for characterizing breast cancer by comprehensively taking the cellularity, vascularity, and microstructure of tissues into account, thus having potential clinical utility in the prognostic estimation and personalized treatment strategies.
		Computer Number: 43 2000. Prediction of response to targeted therapy in HER2-positive breast cancer using MR radiomics View Presentation Video T-N Hung, C-F Lee, W-P Wu, C-F Lu National Yang Ming Chiao Tung University, Taipei, Taiwan Impact: This study demonstrated that combining raw and subtracted dynamic contrast enhancement images could enhance response prediction to targeted therapy in HER2-positive breast cancer. Our findings can facilitate personalized treatment for breast cancer patients.
		Computer Number: 44 2001. Advanced DWI Models for Early Prediction of Pathologic Complete Response in Breast Cancer Treatment: Preliminary Results in a Multicenter Trial View Presentation Video D. Biswas, P. Bolan, D. Malyarenko, J. Ricks, M. Bryant, I. Li, A. Kazerouni, B. Moloney, X. Li, D. Turley, J. Specht, S. Dintzis, H. Rahbar, T. Chenevert, J. Holmes, W. Huang, S. Partridge University of Washington, Seattle, United States Impact: ADC and advanced DWI models demonstrate accurate early prediction of pathologic response in breast tumors undergoing neoadjuvant chemotherapy. Results support their potential as imaging biomarkers to help optimize breast cancer treatments in the future.
		Computer Number: 45 2002. Early MRI markers of response to neoadjuvant endocrine therapy for patients with hormone receptor-positive/HER2-negative breast cancer View Presentation Video N. Onishi, W. Li, J. Gibbs, T. J. Bareng, P. Metanat, J. Kornak, R. Mukhtar, K. Ray, B. Joe, T-S 2. Imaging Working Group, T-S 2. Investigator Network, L. Esserman, A. J. Chien, N. Hylton University of California, San Francisco, San Francisco, United States Impact: Functional tumor volume (FTV) and background parenchymal enhancement (BPE) derived from baseline and 3-week MRI predicted the final FTV after neoadjuvant endocrine treatment (NET) for patients with HR+/HER2– breast cancer. These metrics may be non-invasive early markers for NET.
		Computer Number: 46 2003. Ensemble Machine Learning Model to Classify Pathologic Complete Response from Pre-NAC Breast Cancer DCE-MRI Pharmacokinetic Maps View Presentation Video A. Bhowmik, S. Thakur, P. Kapetas, D. Giri, B. Williams, K. Pinker, S. Eskreis-Winkler Memorial Sloan Kettering Cancer Center, New York, United States Impact: A machine learning model to classify pCR from pre-NAC breast cancer DCE-MRI enables early prediction of treatment response. This would allow clinicians to make timely treatment adjustments and to alter treatment plans to lower toxicity and improve patient outcomes.
		Computer Number: 47 2004. New biomarkers’ exploration with multiparametric MRI for predicting pathological response to neoadjuvant chemotherapy in breast cancer Y. Li, J. Guo, Y. Xue, L. Xie, H. Lu Tianjin Medical University Cancer Institute and Hospital, Tianjin, China Impact: The impact of this study is significant, as it enhances breast cancer treatment by providing a reliable predictive model for neoadjuvant chemotherapy response, potentially improving patient outcomes and treatment strategies.
		Computer Number: 48 2005. Development and validation of a nomogram model for predicting disease-free survival in breast cancer patients after neoadjuvant chemotherapy View Presentation Video S. Chen, S. Che, M. Yang, Y. Chen, S. Wang, J. Li Cancer Hospital Chinese Academy Of Medical Sciences, Shenzhen Center, Shenzhen, China Impact: This nomogram model facilitates early identification of high-risk recurrence factors, enabling personalized treatment and potentially improving survival outcomes for breast cancer patients post-NAC.

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