Computer Number: 17

D. Biswas, V. Park, D. Hippe, D. Turley, K. Widner, T. Yangchen, I. Li, M. Bryant, J. Peeters, H. Rahbar, S. Partridge
University of Washington, Seattle, United States

Impact: This study demonstrated that multishot EPI breast DWI can significantly reduce geometric distortions and potentially improve lesion conspicuity over single-shot EPI DWI, especially using an AI based reconstruction technique to increase spatial resolution without adding to scan time.

Computer Number: 18

Y. Jo, M. Iima, Y. Kato, Y. Zhang, H. Satake, Y. Sato, K. Ichikawa, S. Ishigaki, R. Hyodo, Y. Ichiba, S. Naganawa
Nagoya University Graduate School of Medicine, Nagoya, Japan

Impact: This study demonstrates diagonal DWI as a time-efficient alternative to traditional DWI in breast imaging protocols, potentially improving clinical workflow while maintaining diagnostic accuracy.

Computer Number: 19

B. Moloney, A. Tudorica, D. Biswas, A. Kazerouni, J. Holmes, S. Partridge, W. Huang, X. Li
Oregon Health & Science University, Portland, United States

Impact: Use of model-based BAT at voxel level for DCE-MRI PK modeling may potentially improve accuracy of estimated K^trans and its predictive performance for NAC response.

Computer Number: 20

M. Tang, J. Liu, D. Chen, J. Zhang
Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China

Impact: We proposed a novel model based on longitudinal MRI data to predict pCR to NAC in breast cancer, including both mass and non-mass enhancement lesions, to inform personalized clinical treatment plans.

Computer Number: 21

Q. Zhang, D. Romano, R. Hu, B. Weppner, T. Nguyen, P. Spincemaille, Y. Wang
Weill Cornell Medicine, New York, United States

Impact: QTMnet can be coupled into clinical breast cancer practice to predict treatment response.

Computer Number: 22

T. Zhan, J-k Dai, Q-q Wen, C-h Lu
Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China

Impact: APTWI could be used as non-invasive biomarker for identifying HER2-zero,HER2-low, and HER2-oe. The application of APTWI would be beneficial for guiding treatment selection and dynamically monitoring HER2 expressing level throughout the course of treatment for BC patients.

Computer Number: 23

W. Li, J. Gibbs, N. Le, P. Metanat, M. Gibbons, T. Bareng, N. Onishi, L. Wilmes, E. Price, B. Joe, J. Kornak, C. Yau, D. Wolf, M. J. Magbanua, S. Venters, B. LeStage, L. van 't Veer, A. DeMichele, L. Esserman, N. Hylton
University of California, San Francisco, San Francisco, United States

Impact: This is the first study of MRI-based predictive models that were developed and validated using two separate large cohorts from a multicenter neoadjuvant chemotherapy clinical trial.

Computer Number: 24

J. Zimmermann, B. Daniel, B. Hargreaves, C. Moran
Stanford University, Stanford, United States

Impact: In supine breast MRI, B0 inhomogeneity and B1⁺ variations and respiratory-induced motion all increase, posing new technical challenges that must be addressed for robust supine breast imaging, and to ultimately overcome limitations of standard-of-care prone imaging.

Computer Number: 25

M. Gibbons, W. Li, N. Le, J. Gibbs, T. Bareng, N. Onishi, L. Wilmes, E. Price, B. Joe, J. Kornak, C. Yau, D. Wolf, M. J. Magbanua, B. LeStage, J. Perlmutter, D. Yee, W. Symmans, H. Rugo, R. Shatsky, C. Issacs, I-S Investigator Network, I-S Imaging Working Group, L. van 't Veer, A. DeMichele, L. Esserman, N. Hylton
University of California, San Francisco, San Francisco, United States

Impact: In the breast cancer I-SPY2 clinical trial, better pathological complete response (pCR) prediction would lead to improved treatment redirection and treatment sparing, improving patient outcomes. In this study, we optimized parameters for MRI functional tumor volume calculation to improve predictions.

Computer Number: 26

K. Liu, R. Zheng, J. Wang, S. Wang
Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Impact: Our radiomics models underscored the value of multi-region, multi-sequence MRI data in longitudinal monitoring during neoadjuvant chemotherapy (NAC), revealing the potential to guide personalized NAC regimens for patients. It is necessary to confirm this potential through prospective comparative studies.

Computer Number: 27

J. Yu, S. Du, H. Guan, L. Zhang
The Fourth Affiliated Hospital of China Medical University, Shenyang, China

Impact: This research highlights R2* values from IDEAL-IQ MRI as valuable, early biomarkers for breast cancer chemotherapy response, potentially guiding clinical decisions, enhancing personalized treatment approaches, and contributing to better patient outcomes by reducing unnecessary or ineffective therapies.

Computer Number: 28

H. Zhou, D. y. Yang, X. c. Zeng, X. d. Liu, L. Wei
The Affiliated Jinyang Hospital of Guizhou Medical University, gui yang, China

Impact: This study enhances breast cancer diagnosis by providing a noninvasive method to differentiate Luminal A and Luminal B subtypes. This could potentially improve clinical decision-making and enable personalized treatment plans for patients.

Computer Number:

Computer Number: 29

Z. Ren, X. Fan, F. Howard, R. Nanda, H. Abe, K. Kulkarni, N. Chen, A. Biernacka, G. Karczmar
University of Chicago, Chicago, United States

Impact: K-means clustering analysis of ultrafast DCE-MRI is a stable technique to effectively predict treatment response in breast cancer patients prior to neoadjuvant chemotherapy, which facilitates personalized therapy adjustments and can improve clinical outcomes through individualized treatments.

Computer Number: 30

S. Chumsaengsri, K. Kulkarni, Z. Ren, H. Abe, G. Karczmar
University of Chicago, Chicago, United States

Impact: Ultrafast imaging (3.5–4.6 seconds) yields benefit individuals with preserved diagnostic accuracy for breast cancer detection. Furthermore, lower signal enhancement of background parenchyma by using gadoterate meglumine may help for lesion detection in screening settings, particularly in young or premenopausal women.

Computer Number: 31

Y. Zhou, J. Li, M. Chen, N. Zhou, Y. Wang, Y. Zeng, Y. Lu
Medical imaging department，First Affiliated Hospital of Kunming Medical University, Kunming 650000, China

Impact: MDME-based T1 and T2 mappings combined with DWI offers a rapid, non-invasive method for predicting HER-2 expression in breast cancer, potentially reducing the need for biopsy and supporting preoperative decision-making.