View Presentation Video

Debosmita Biswas¹, Jin You Kim^1,2, Isabella Li¹, Michaela R DelPriori³, Dallas Turley⁴, Mary Lynn Bryant¹, Wei Huang⁵, Habib Rahbar¹, and Savannah C Partridge^1
1Radiology, University of Washington, Seattle, WA, United States, ²Radiology, Pusan National University Hospital, Busan, Korea, Republic of, ³Bio Engineering, University of Washington, Seattle, WA, United States, ⁴Philips Healthcare, Bothell, WA, United States, ⁵Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques, Diffusion Weighted Imaging

Diffusion weighted imaging (DWI) is emerging as a viable tool for non-contrast MRI breast cancer screening, but it is unclear what factors on DWI impact lesion detectability. In this prospective clinical trial, we evaluated lesion and imaging factors that affected cancer detection.  Cancers were overall more detectable at higher b=1200 vs b=800 s/mm², but the background parenchymal signal (BPS) impacted cancer visibility at the higher b value. Cancer histologic type also impacted detectability on DWI. Overall, our findings suggest that interpretation at higher b values and further technical refinements to reduce appearance of BPS may help improve DWI sensitivity.

View Presentation Video

Catherine J. Moran¹, Matthew J. Middione¹, Valentina Mazzoli¹, Jessica A. McKay-Nault¹, Arnaud Guidon², Uzma Waheed¹, Eric L. Rosen¹, Steven P. Poplack¹, Jarrett Rosenberg¹, Daniel B. Ennis¹, Brian A. Hargreaves¹, and Bruce L. Dnaiel^1
1Radiology, Stanford University, Stanford, CA, United States, ²General Electric Healthcare, Boston, MA, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques

Diffusion-Weighted Imaging (DWI) allows for the detection of breast cancer without a contrast injection, while supine positioning may improve the comfort and efficiency of a breast MRI screening exam. This work investigates multi-shot DWI of the breasts in the supine versus prone positions, in both asymptomatic volunteers and patients with breast lesions. Supine multi-shot DWI outperformed prone multi-shot DWI based on an image quality observer study, receiving significantly higher ratings for sharpness, aliasing, and overall image quality. Lesion Apparent Diffusion Coefficients (ADCs) were highly correlated between the two positions, while fibroglandular tissue ADCs were significantly higher in the supine position.

View Presentation Video

Stephane Loubrie¹, Maren Andreassen², Ana Rodriguez-Soto¹, Michael Carl³, Summer Batasin¹, Christopher Conlin¹, Tyler Seibert^1,4,5, Michael Hahn¹, Joshua Kuperman¹, Anders Dale^1,6, and Rebecca Rakow-Penner^1,5
1Radiology, University of California, San Diego, San Diego, CA, United States, ²Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ³Global MR Application and Workflow, GE Healthcare, Boston, MA, United States, ⁴Radiation medicine, University of California, San Diego, San Diego, CA, United States, ⁵Bioengineering, University of California, San Diego, San Diego, CA, United States, ⁶Neurosciences, University of California, San Diego, San Diego, CA, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques

Diffusion weighted Imaging (DWI) acquisitions are often part of breast cancer MRI protocols. The signal intensity decrease as a function of the b-value in diffusion imaging is non-linear and complex with inconsistent Apparent Diffusion Coefficients. In this abstract, we investigate ADC mapping computation and accuracy. Mapping techniques gave similar performances, with comparable medians and standard deviations.

View Presentation Video

Aika Okazawa¹, Mami Iima^1,2, Masako Kataoka¹, Ryosuke Okumura³, Sachiko Takahara⁴, Tomotaka Noda⁵, Taro Nishi⁵, Takayoshi Ishimori³, Maya Honda^1,6, and Yuji Nakamoto^1
1Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, ²Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan, ³Department of Diagnostic Radiology, Kitano Hospital, Tazuke Kofukai Medical Research Institute, Osaka, Japan, ⁴Department of Breast Surgery, Kitano Hospital, The Tazuke Kofukai Medical Research Institute, Osaka, Japan, ⁵Department of Clinical Radiology Service, Kitano Hospital, The Tazuke Kofukai Medical Research Institute, Osaka, Japan, ⁶Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan

Keywords: Breast, Diffusion/other diffusion imaging techniques

 We propose a DWI-reading method based on the an adjusted BI-RADS lexicons  using five b values of 0, 200, 800, 1000, and 1500 s/mm² (5b-value-DWI). Diagnostic performance was evaluated in comparison with BI-RADS lexicons from standard BI-RADS DCE MRI and a DWI-reading method based on the adjusted BI-RADS lexicons using two b values of 0, 800 s/mm² (2b-value-DWI).Our proposed DWI reading methods achieved diagnostic performance comparable to the standard BI-RADS. With higher sensitivity and NPV in 5b-value-DWI compared to 2b-value-DWI, DWI reading with 5b-values has the potential to increase diagnostic confidence in differentiating malignant and benign breast tumors.

View Presentation Video

Yun Su¹, Xiang Zhang¹, Huijun Hu¹, Lingjie Yang¹, Yu Wang¹, Chen Zhao², and Yishi Wang^3
1Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China, ²Philips Healthcare, Guangzhou, China, ³Philips Healthcare, Beijing, China

Keywords: Breast, Breast, Tumor, Diffusion, OGSE, PGSE

Defining the nature of benign and malignant breast tumors is essential to reduce unnecessary biopsies of benign tumors. Magnetic resonance imaging (MRI) is the most sensitive imaging modality for evaluating breast carcinoma lesions, but its specificity is relatively low. This study investigated the significance of the parameters for the differential diagnosis of mammary mass using time-dependent diffusion MRI. Results showed significant cell size differences between benign and malignant space-occupying lesions, with high accuracy (85.7%), specificity (83.3%), sensitivity (87.5%) and AUC (0.823). This provides a new direction for noninvasively differentiating benign and malignant lesions of the mammary gland.

View Presentation Video

Jie Ding^1,2,3, Zhen Zhang³, Yajia Gu^1,2, Yishi Wang⁴, Xiuzheng Yue⁴, Dazhi Chen³, Rongrong Zhu³, and Ruoshui Ha^3
1Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, ²Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, ³People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China, ⁴Philips Healthcare, Beijing, China

Keywords: Breast, Cancer

Some novel biomarkers have emerged from the fact that the ADC depends strongly on the diffusion time, such as the rate of ADC change using two diffusion times for the differentiation of breast tumor types and cell size modeling. In this study, we propose to use Multiband (MB) SENSE to accelerate the data acquisition of OGSE and PGSE sequences and compare the diagnostic value of ADC at different diffusion times as  well as cell size imaging parameters using IMPULSED model for breast tumor. Our data showed vin and cellularity and the rate of ADC change have superior diagnostic performance.

View Presentation Video

Zhu Kaiguo¹, Wang Pingping¹, Dang Yanli¹, Wang Lifang¹, Liu Rumei¹, Chen Baoying¹, and shaoyu wang^2
1Xi'an International Medical Center Hospital, Xi'an, China, ²MR Scientific Marketing, Siemens Healthineers, Shanghai, China

Keywords: Breast, Diffusion/other diffusion imaging techniques

DWI performs an important role in the diagnosis of the benign and malignant breast lesions. This study compared the overall image quality, SNR,CNR and the diagnostic performance of ADC values between ZOOMit DWI and conventional DWI in breast cancer with same geometric parameters. The result showed that ZOOMit DWI provided significantly higher image quality and lesion conspicuity than C-DWI with no difference diagnostic performance for ADC values and with almost the same scanning time, which can help to improve the diagnosis efficiency in breast cancer.

View Presentation Video

jinrui liu¹, Bing Chen², mengyin xu², zhihao li³, and zhaohui an^2
1Ningxia Medical University, yin chuan, China, ²General Hospital of Ningxia Medical University, yinchuan, China, ³GE Healthcare China, xian, China

Keywords: Breast, Cancer

The ADC histogram parameters based on the MUSE-DWI are important indicators for breast cancer diagnosis. ADC_5% was more accurate for lesion discrimination than ADC_MUSE.

View Presentation Video

Stephane Loubrie¹, Ana Rodriguez-Soto¹, Michael Carl², Summer Batasin¹, Christopher Conlin¹, Tyler Seibert^1,3,4, Michael Hahn¹, Joshua Kuperman¹, Arnaud Guidon², Anders Dale^1,5, Haydee Ojeda-Fournier¹, and Rebecca Rakow-Penner^1,4
1Radiology, University of California, San Diego, San Diego, CA, United States, ²Global MR Application and Workflow, GE Healthcare, Boston, MA, United States, ³Radiation medicine, University of California, San Diego, San Diego, CA, United States, ⁴Bioengineering, University of California, San Diego, San Diego, CA, United States, ⁵Neurosciences, University of California, San Diego, San Diego, CA, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques

Diffusion-weighted imaging holds great potential in improving specificity in breast cancer MRI, potentially reducing the number of unnecessary biopsies. Additionally, breast cancer screening protocols would benefit from high-resolution DWI acquisitions, especially in the through-plane direction. In this abstract we explore multi-slice excitation as a promising parallel-imaging tool to improve through-plane image resolution.

View Presentation Video

Huan Chang¹, Dawei Wang², Yuting Li³, Shaoxin Xiang⁴, Ke Xue⁴, Peng Kong⁵, and Qingshi Zeng^2
1Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Shandong, China, Jinan, China, ²Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Shandong, China, Jinan, China, ³Department of Radiology, The First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Shandong, China, Jinan, China, ⁴MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China, Shanghai, China, ⁵Department of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Shandong, China, Jinan, China

Keywords: Breast, Diffusion/other diffusion imaging techniques

Accurately differentiating breast cancer is crucial for clinical treatment. However, it is challenging due to the high heterogeneity of breast cancer. This study used a continuous-time random-walk (CTRW) diffusion model to characterize breast lesions and their molecular subtypes. The malignant lesions had significantly lower ADC, Dm, and α values than benign lesions. Significant differences were also found between luminal A and triple-negative groups in parameter β. And the combination of CTRW parameters yielded better performance in classifying breast cancer than ADC. Therefore, this study highlights that the CTRW parameters are superior to ADC in distinguishing breast lesions and molecular subtypes.

Hui Feng¹, Hui Liu¹, Qi Wang¹, Mengyu Song¹, Tianshu Yang², Liyun Zheng², Dongmei Wu³, Xian Shao⁴, and Gaofeng Shi^1
1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China, ³Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China, ⁴Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China

Keywords: Breast, Diffusion/other diffusion imaging techniques

Breast cancer is a common cancer that severely threatens the health of women worldwide. The advanced diffusion model using high b-values enables a more comprehensive description of the tumor tissue by cellularity and heterogeneity. In current study, a continuous-time random-walk (CTRW) model was applied to identify malignancy of breast lesions and the association between model-derived parameters and immunohistochemical indices was evaluated. All model-derived parameters could identify tumor malignancy, combined parameter could further discriminate ER+/ER- and PR+/PR- patients, while temporal heterogeneity parameter was significantly correlated with PR expression. The CTRW model has demonstrated potential value in breast cancer diagnosis and prognosis.

View Presentation Video

Huan Chang¹, Dawei Wang², Yuting Li³, Shaoxin Xiang⁴, Ke Xue⁴, Peng Kong⁵, and Qingshi Zeng^2
1Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China, ²Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China, ³Department of Radiology, The First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China, ⁴MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China, ⁵Department of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China

Keywords: Breast, Diffusion/other diffusion imaging techniques

Breast cancer is a heterogeneous tumor, and its treatment is customized determined by the pathologic and tumor molecular features. This study used a continuous-time random-walk (CTRW) diffusion model to characterize prognostic factors status of breast lesions. The parameter β showed significantly differences in ER and PR status and showed significant correlation with ER and PR expression, and the combination of Dm, α, and β produced the best performance in identifying ER and PR status. Our study demonstrated that the CTRW parameters as potential biomarkers were superior to conventional ADC in predicting prognostic factors of breast cancer.

Yanjin Qin¹, Yunfei Zhang², Yongming Dai², and Tao Ai^1
1Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ²MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Breast, Cancer

This study aimed to assess the diagnostic performance of restriction spectrum imaging (RSI)-derived parameters for distinguishing benign from malignant breast lesions compared to conventional DWI values and further evaluate the associations between RSI-derived parameters and prognostic factors of breast cancer. The results indicated the RSI-derived parameters (f1, f3, and f1f2) may facilitate the differential diagnosis between benign and malignant breast lesions.

View Presentation Video

Muge Karaman^1,2, Yangyang Bu^3,4, Guangyu Dan^1,2, Zheng Zhong¹, Qingfei Luo¹, Shiwei Wang^3,4, Changyu Zhou^3,4, Weihong Hu^3,4, X. Joe Zhou^1,2,5, and Maosheng Xu^3,4
1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States, ³The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China, ⁴The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, ⁵Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Keywords: Breast, Breast, molecular subtype prediction, heterogeneity, high-b-value diffusion MRI

Breast cancer exhibits a wide spectrum of molecular subtypes and, which has important implications in treatment strategies. In this study, we used an integrated diffusion-weighted imaging approach for simultaneous assessment of tissue cellularity, vascularity, and heterogeneity – DISMANTLE – to predict molecular subtypes and prognostic factors of breast cancer. We investigated the feasibility of using the histogram features of the cellularity-, vascularity-, and heterogeneity-related parameters of DISMANTLE for differentiation between luminal-A and luminal-B and HER2+ and HER2- breast cancer.

View Presentation Video

Maya Gilad¹ and Moti Freiman^2
1Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel, ²Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel

Keywords: Breast, Diffusion/other diffusion imaging techniques

Early prediction of pathological complete response (pCR) following neoadjuvant chemotherapy for breast cancer plays a critical role in surgical planning and optimizing treatment strategies. Recently, machine and deep-learning based methods were suggested for early pCR prediction from multi-parametric MRI data with moderate success. We introduce PD-DWI, a physiologically-decomposed DWI machine-learning model to predict pCR from DWI and clinical data. Our model first decomposes the raw DWI data into the various physiological cues that are influencing the DWI signal and then uses the decomposed data, in addition to clinical variables, as the input features of a radiomics-based XGBoost model.

View Presentation Video

yuanfei li¹, ning ning¹, lina zhang¹, qi wu¹, siqi zhao¹, ailian liu¹, qinhe zhang¹, jianzhou chen², jingjing cui², and lizhi xie^3
1The First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China, ³GE Healthcare, Beijing, China

Keywords: Breast, Radiomics

Mucinous breast carcinomas (MBCs) always have similar morphological manifestations with invasive ductal breast carcinomas (IDCs) on conventional MRI. MBCs have a better prognosis and early treatment can achieve good results. Radiomics is a novel tool automated data feature extraction algorithms，which has the potential to uncover disease characteristics that are difficult to identify by human vision alone. We develop a radiomics model for explore the preoperative differentiation of combining the intratumoral signal intensity (SI) on T2WI and ADC derived from DWI in MBCs and IDC. The results confirm that the model has potential in the differential diagnosis of MBCs and IDC.

View Presentation Video

Wen Li¹, Julia Carmona-Bozo¹, Lisa J Wilmes¹, Natsuko Onishi¹, Jiachao Liang¹, Jessica E Gibbs¹, Nu N Le¹, Judith Zimmermann¹, David C Newitt¹, Bonnie N Joe¹, John Kornak¹, Savannah C Partridge², Patrick Bolan³, I-SPY 2 Investigator Network⁴, I-SPY 2 Imaging Working Group¹, and Nola M Hylton^1
1University of California, San Francisco, San Francisco, CA, United States, ²University of Washington, Seattle, WA, United States, ³University of Minnesota, Minneapolis, MN, United States, ⁴Quantum Leap Healthcare, San Francisco, CA, United States

Keywords: Breast, Cancer

Acquiring breast diffusion-weighted MRI (DWI) with adequate image quality is a challenge in multi-center clinical trials. The most common issues are inadequate fat suppression, image artifacts, and poor signal-to-noise ratio (SNR). We developed and evaluated a DWI quality ranking system for identifying data with quality issues while preserving analyzable data. The identification of artifacts and adequate SNR had the highest and lowest inter-reader agreement, respectively. Removing inferior quality data improved prediction of pathologic outcome following neoadjuvant treatment in a multi-center clinical trial. The quality ranking system standardizes identification of poor quality data and informs recommendations for improvement to clinical sites.

Qianqian Feng¹, Weiyin Vivian Liu², Ling Sang¹, Kejun Wang¹, Xingyao Yu¹, Wen Chen¹, and Lin Xu^1
1Department of Radiology,Taihe hospital, Hubei, China, ²GE Healthcare, MR Research, Beijing, China

Keywords: Breast, Diffusion/other diffusion imaging techniques

Our study showed synthetic DW images (syDWIs) with high b-value provided better lesion conspicuity, image quality and contrast to noise (CNR), reduce scan time of DWI acquisitions especially at very high b-value, and improved lesion visualization particularly in dense breast. Moreover, derived ADC values showed good consistency between scanned and synthetic DWIs. Therefore, syDWIs with b value of 800 s/mm² have great potential in elevation of diagnosis confidence and efficacy on tumors and high repeatability of ADC values is helpful in replacement of scanned high-b-value DWIs in clinics despite existed intra-modality, but not inter-modality, difference of manual ROI measures.

View Presentation Video

Lisa J. Wilmes¹, Jessica E. Gibbs¹, Nu N. Le¹, Todor Karaulanov², David C. Newitt¹, Kathryn E. Keenan³, Alina Tudorica⁴, Gwen Bowden⁵, Yucheng Liu⁶, Lauren Fang⁷, Patrick J. Bolan⁸, Dariya Malyarenko⁹, Thomas L. Chenevert⁹, David Van Wie¹⁰, Chelsea Pyle⁴, Bethany Niell⁵, Richard Ha⁶, Rebecca Rakow-Penner⁷, Haydee Ojeda-Fournier⁷, An Church⁸, Bonnie N. Joe¹, and Nola M. Hylton^1
1University of California San Francisco, San Francisco, CA, United States, ²CaliberMRI, Boulder, CO, United States, ³National Institute of Standards and Technology, Boulder, CO, United States, ⁴Oregon Health & Science University, Portland, OR, United States, ⁵Moffitt Cancer Center, Tampa, FL, United States, ⁶Columbia University Medical Center, New York, NY, United States, ⁷University of California San Diego, San Diego, CA, United States, ⁸University of Minnesota Medical School, Minneapolis, MN, United States, ⁹University of Michigan Health System, Ann Arbor, MI, United States, ¹⁰Boulder Labs, Boulder, CO, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques

CaliberMRI’s multiparametric quantitative breast MRI phantom was scanned with a standard protocol, including DWI, at seven clinical sites as part of a quality control program. Protocol adherence was assessed. ADC values for fibroglandular and malignant breast tissue DWI mimics were calculated and corrected for temperature using the qCal software, and summary reports were generated. Inter- and intra-site variations in ADC derived from DWI phantom scans acquired longitudinally over the last 16 months provided information that can be used to troubleshoot imaging acquisition issues with sites and vendors as part of a phantom-based QC program.