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Yan Li^1
1Tongji Hospital, Wuhan, China

Keywords: Breast, Cancer

The diagnosis of NMEs is a challenge for most radiologists based only on morphological information. The value of DWI or DCE curves for discriminating malignant lesions from benign lesions was controversial. We estabished and compared the ADC model (ADC+morphology) and the TIC (TIC+morphology) for detecting malignancy with a relatively large sample size of NME lesions. We found that the TIC model was superior than the ADC model for differentiating between benign and malignant NME lesions. A whole DCE-MRI scan for NMEs is recommended without the need to acquire additional DWI data.

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Jiejie Zhou¹, Haiwei Miao¹, Huiru Liu¹, Zhongwei Chen¹, Youfan Zhao¹, Meihao Wang¹, and Min-Ying Su^2
1First affiliated hospital of Wenzhou Medical University, Wenzhou, China, ²University of California, Irvine, California, CA, United States

Keywords: Breast, Breast

A total of 102 malignant and 60 benign non-mass enhancement lesions were analyzed. Two radiologists with 6 (Reader1) and 14 (Reader2) years of experience assigned the BI-RADS category and Kaiser score (KS). KS decreased biopsy or surgery by 20% for Reader 1 and 16.7% for Reader 2 compared with BI-RADS 4-5. The sensitivity, specificity and accuracy were 87.2%, 78.3% and 84.0% for Reader 1, and 85.2%, 83.3% and 84.6% for Reader 2. Using cutoff threshold of KS>4, the specificity of modified KS increased by 6.7% for both readers, increasing the accuracy slightly by 1.7%-2.5% and keeping the same sensitivity.

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Chun Lian¹, Lulu Zhuang¹, Zehao Wang ¹, Zhigang Wu², Yi Dai¹, and Rong Huang^1
1Radiology, Peking University Shenzhen Hospital, Shen Zhen, China, ²Clinical & Technical Support, Philips Healthcare (Shenzhen) Ltd, Shen Zhen, China

Keywords: Breast, Breast

Breast cancer is the most prevalent cancer among women ,early detection and timely treatment are the key to imprve survival rate. In this preliminary study, we aimed to differentiation lesions using native T1mapping. The mean T1 relaxation time was higher in the malignant group compared to the benign group by both observers (p<0.05). Results indicated that native T1mapping could be used to differentiate benign and malignant lesions, which might be promising in clinical applications. However, the combination of T1mapping and DWI dose not improve the AUC statistically significantly.

 

Xiaoyan Liu^1,2, Baojian Wang², Litao Zhang¹, Zhenbo Ma¹, Yanlei Wang², Yishi Wang³, Xiujuan Li¹, and Yuanzhong Xie^1
1Taian City Central Hospital, Taian, China, ²Shandong First Medical University, Taian, China, ³Philips Healthcare, Beijing, China

Keywords: Breast, CEST & MT, Tumor;Amide proton transfer imaging;

According to the molecular classification of breast cancer, the clinical therapeutic strategy is formulated. Currently, the diagnostic value of amide proton transfer (APT) imaging in breast cancer is still unclear. This study aims to explore the diagnostic value of APT, DWI, and DCE-MRI in the molecular classification and prognosis of breast cancer. Our results showed that there were differences in TTP and BE values between luminal and non-luminal subtypes of breast cancer. The combination of APT, DWI, and DCE-MRI had the highest diagnostic efficiency. APT, DWI, and DCE-MRI parameters were correlated with different prognostic factors of breast cancer.

Fang Xiao¹, Mingli Jin², Peng wang¹, Zhihua Pan², Qi Zhao², Ying Jiang², Miaoqi Zhang³, and Yong Zhang^3
1Department of Radiology,The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China, ²The Second Affiliated Hospital of Chengdu Medical College, Nuclear Corportation 416 Hosptital, chengdu, China, ³GE Healthcare, MR Research, Beijin, China

Keywords: Breast, Cancer, contrast-enhanced (DCE) MRI, sentinel lymph nodes

Sentinel lymph nodes(SLN) status is one of the most important indicators affecting breast cancer patient treatment and prognostic. Thus, a non-invasive diagnostic method capable of accurately predicting of the SLN status is of great value in selecting breast cancer patients who would benefit from therapy. In this study, we investigate the diagnostic performance of different SLN status [pN0, pN0 (i+), pN1mi, and pN1] using dynamic contrast-enhanced (DCE) MRI.We found that (1) Krans, Kep, and Ve are significantly different in different SLN status, (2) and the Ktrans is the best single parameter for the detection of SLN status.

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Rie Ota¹, Masako Kataoka², Mami Iima², Maya Honda³, Aika Okazawa², Yosuke Yamada⁴, Yasuhide Takeuchi⁴, Masakazu Toi⁵, Takeshi Kubo¹, and Yuji Nakamoto^2
1Department of Radiology, Tenri Hospital, Nara, Japan, ²Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University graduate school of medicine, Kyoto, Japan, ³Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan, ⁴Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan, ⁵Department of Breast Surgery, Kyoto University Hospital, Kyoto, Japan

Keywords: Breast, Cancer, disease-free survival

  This study aimed to investigate the association of DWI score of breast cancer derived from MRI obtained after neoadjuvant systemic treatment (NST) with disease-free survival (DFS). Kinetic score from standard DCE-MRI at the same timing was analyzed for comparison. Kaplan-Meier analysis showed that patients with DWI score of 2 showed significantly shorter DFS than those with DWI score 0 or 1. Patients with kinetic score of 2 or 3 showed significantly shorter DFS than those with kinetic score of 0 or 1. DWI score demonstrated excellent inter-reader agreement and is suitable for predicting prognosis of breast cancer patients after NST.

Rajat Thawani¹, Lina Gao², Ajay Mohinani³, Alina Tudorica⁴, Xin Li⁵, Zahi Mitri¹, and Wei Huang^5
1Hematology and Oncology, Oregon Health and Science University, Portland, OR, United States, ²Biostatistics Shared Resource, Oregon Health and Science University, Portland, OR, United States, ³Medicine, Oregon Health and Science University, Portland, OR, United States, ⁴Radiology, Oregon Health and Science University, Portland, OR, United States, ⁵Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, United States

Keywords: Breast, Cancer

Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT.

Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965.

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Isobel Gordon^1,2, George Ralli², Amy Herlihy², Carolina Fernandes², Amy O'Brien³, Sally Collins^1,2, and Michael Brady^2,4
1Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, ²Perspectum Ltd., Oxford, United Kingdom, ³Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, United Kingdom, ⁴Department of Oncology, University of Oxford, Oxford, United Kingdom

Keywords: Breast, Fat, Breast density

Breast density is a well-established risk factor for breast cancer but conventional visual assessment using mammography has high operator variability. Proton density fat fraction (PDFF) derived from chemical shift encoded (CSE)-MRI has recently been proposed as a quantitative, non-ionising, operator-independent tool for the calculation of breast density. This study demonstrates that breast density derived from PDFF maps which account for a breast-specific fat spectrum can distinguish between the latest density categorisations used in clinical practise and thereby may have use in cancer risk assessment and as a determinant of clinical pathways.

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Jia Ying¹, Renee Cattell^1,2, and Chuan Huang^1,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Radiation Oncology, Stony Brook Medicine, Stony Brook, NY, United States, ³Radiology, Stony Brook Medicine, Stony Brook, NY, United States

Keywords: Breast, Breast

The current standard of care for breast density assessment using mammography according to the Breast Imaging Reporting and Data System (BI-RADS) is subjective and can also suffer from intra- and inter-reader variability. Quantitative measurement of breast density is crucial for personalized risk assessment and longitudinal monitoring. Our team has previously developed a quantitative, MR-derived BD (MagDensity) measurement based on fat-water decomposition, which has shown high accuracy and test-retest reproducibility within a single scanner. In order for this measure to be practical for clinical adoption, this study aims to determine the reliability of MagDensity across scanners, field strengths, and vendors.

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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, Diffusion/other diffusion imaging techniques

Both the IMPULSED and MRI-cytometry approaches are based on temporal diffusion spectroscopy (TDS), which connotes the acquisitionof dMRI data with a range of diffusion times expressed in terms of components of a diffusion spectrum.MR cell size imaging is possible with IMPULSED and MRI-cytometry using the theory of TDS. In this study, we proposed to use multiband to accelerate the data acquisition for TDS and compared the IMPULSED quantitative parameters and derivative ADC spectrum for two protocols. Our results showed the consistency in IMPULSED quantitative parameters and ADC calculation for the multiband accelerated protocol with the results without using multiband.

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Ning Ning¹, Lina ZHANG¹, Zhuo WANG¹, Qi WU¹, Hongbing LIANG¹, Qingwei SONG¹, Ailian LIU¹, and Yiming Wang^2
1First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Clinical and Technical Support,PhilipsHealthcare, Shanghai, China

Keywords: Breast, Breast

 Dynamic contrast-enhanced (DCE) MRI is a multi-phase sequence that is limited by long scan times, which makes acquisition acceleration necessary to accomplish clinical feasibility. The aim of this study was to investigate the effect of different Acceleration Factors (AFs) on the image quality of isotropic DCE-MRI acquired with the Compressed SENSE (CS) technique, and to find the optimal AF for the isotropic DCE-MRI.CS factors of 4 to 7 were compared against the conventional SENSE technique in 43 patients. Preliminary results show a CS factor of 5 is the optimal AF with optimized image and velocity data quality.

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Yazan Ayoub¹, Sai Man Cheung¹, Boddor Maglan¹, Ehab Husain², Yazan Masannat³, and Jiabao He^1,4
1Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom, ²Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, ³Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, ⁴Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom

Keywords: Breast, Cancer

Micro-calcification is a central feature of breast cancer, and clinically revealed on mammography. Mammography, a two dimensional imaging approach, is inadequate to provide refined classification. Conventional MRI cannot capture the rapid signal decay from micro-calcification, with Ultra Short Echo Time (UTE) developed to image short T2* species. UTE demands precise control of the hardware to minimise the time delay between radiofrequency transmission and image acquisition, while substantial effort is required to optimise image quality and contrast. We set out to examine the degree of calcification in breast tumour specimens freshly excised from patients using UTE, with comparison against histopathological findings.

Shuyi Peng¹, Peng Sun², Zhigang Wu², and Fan Yang^1
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ²Philips Healthcare, Beijing, China

Keywords: Breast, Diffusion/other diffusion imaging techniques, OGSE; IMPULSED; Breast tumor

This study aimed to investigate the feasibility of temporal diffusion spectroscopy (the IMPULSED technique) for breast imaging. The result shows that the diffusion MRI–based microstructural mapping can evaluate the cell size in breast tumors and may demonstrate pathologic findings promise for characterizing breast cancer.

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Jonghyun Bae^1,2,3,4, Zhengguo Tan⁵, Zhengnan Huang¹, Laura Heacock^2,3, Linda Moy^2,3, Florian Knoll⁵, and Sungheon Gene Kim^4
1Vilcek Institute of Graduate Biomedical Science, NYU School of Medicine, New York, NY, United States, ²Center for Biomedical Imaging, Radiology, NYU School of Medicine, New York, NY, United States, ³Center for Advanced Imaging Innovation and Research, Radiology, NYU School of Medicine, New York, NY, United States, ⁴Radiology, Weill Cornell Medical College, New York, NY, United States, ⁵Biomedical Engineering, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany

Keywords: Breast, DSC & DCE Perfusion

Reconstruction of highly accelerated dynamic images is challenging and requires rigorous validation of reconstruction methods. We proposed a digital reference object (DRO) toolkit that provides realistic morphology and contrast dynamics in breast cancer. We acquired various base images containing real breast cancer lesions and simulated realistic contrast dynamics using the estimated kinetic parameters. Our toolkit provides a large number of reference objects with wide ranges of the dynamic images, kinetic parameters, segmentation masks, and k-space data. These realistic DROs with known ground-truth values can be used for different studies, including validation of reconstruction and training deep neural network.

Zhuo Wang¹, Lina Zhang¹, Qi Wu¹, Ning Ning¹, Hongbing Liang¹, Qingwei Song¹, Qinhe Zhang¹, Ailian Liu¹, and Peng Sun^2
1First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Clinical and Technical Support, Philips Healthcare, WuHan, China

Keywords: Breast, MR Value

The histological grade of invasive breast cancer should be obtained by invasive needle biopsy , and the extracted tissue might not represent the heterogeneity of the whole tumor. Therefore, a novel nomogram was built from mDixon-Quant MRI-based radiomics features and traditional clinical image characteristics for grading invasive ductal breast cancer. The AUC of the combined model reached 0.907, which was significantly higher than that of the model only using traditional clinical image characteristics (0.782) or radiomics features (0.834). The proposed method can be an effective supplement to conventional breast MRI in predicting the histological grade of invasive ductal breast cancer.

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Caroline Wilpert¹, Hannah Schneider¹, Claudia Neubauer¹, Thomas Benkert², Elisabeth Weiland², Ralph Strecker³, Marco Reisert⁴, Jakob Weiß¹, Matthias Benndorf¹, Fabian Bamberg¹, Marisa Windfuhr-Blum¹, and Jakob Neubauer^1
1Departement of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany, ²MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ³EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany, ⁴MR Physics, University Hospital Freiburg, Freiburg im Breisgau, Germany

Keywords: Breast, Diffusion/other diffusion imaging techniques

This prospective study evaluated image quality features of a novel accelerated DWI with deep learning image reconstruction in 3T breast MRI in a clinical setting in direct comparison to conventional DWI. Deep learning DWI (DL-DWI) shows a drastically shortened acquisition time of 46% compared to standard DWI, while maintaining a high image quality. Even though some image quality features were rated superior in standard DWI, image quality remained good for DL-DWI and lesion conspicuity scores were rated superior for DL-DWI compared to conventional breast DWI. Therefore, DL-DWI seems a feasible technique for accelerated breast DWI. 

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Saurabh Garg¹, Saqib Basar¹, Nasrin Akbari¹, Thanh-Duc Nguyen¹, Sean London², Yosef Chodakiewitz², Rajpaul Attariwala¹, and Sam Hashemi^1
1Voxelwise Imaging Technology Inc, Vancouver, BC, Canada, ²Prenuvo, Vancouver, BC, Canada

Keywords: Breast, Machine Learning/Artificial Intelligence, Fatty tissue, Screening

BI-RADS breast tissue composition defines which imaging modality is best suited for tissue examination. However it is subjective and varies between readers whereas AI techniques have been shown to remove subjectivity. We evaluate the use of state-of-the-art AI algorithms on a general whole-body noncontrast MRI to quantify the amount of fat versus nonfat tissue and compare with radiologists reports. Our results show significant correlation between the AI and radiologists' decisions. Further, we show on large dataset that  the rate of replacement of nonfat fibroglandular tissue with fatty tissue is almost triple the rate in premenopausal  women than postmenopausal women.

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Suneeta Chaudhary¹, Elizabeth Lane², Eileen Chang³, Anika McGrath², Eralda Mema², Allison Levy⁴, Melissa Reichman², Katerina Dodelzon⁴, Marcel Dominik Nickel⁵, Linda Moy⁶, Michele Drotman², and Sungheon Gene Kim^1
1Radiology, Weill Cornell Medical College, New York, NY, United States, ²Weill Cornell Medical College, New York, NY, United States, ³Weill Cornell Medical college, New York, NY, United States, ⁴Weill Cornell Medical College, New york, NY, United States, ⁵MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ⁶New York University School of Medicine, New York, NY, United States

Keywords: Breast, Cancer

The purpose of this study is to develop a non-invasive imaging method to measure the fatty acid composition (FAC) of mammary adipose tissue (MAT) and to investigate its role in breast cancer. A novel unsupervised deep learning approach has been developed using the MRI signal equation of fat peaks in the loss function to generate the FAC maps without using any training data. It takes less computational efforts than conventional voxel-wise analysis techniques. The repeatability and reproducibility of the proposed method have been examined on six subjects, which showed no statistically significant difference between repeated analyses and scans.

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yanhong chen¹, lijun wang¹, ran luo¹, huanhuan liu¹, and dengbin Wang^1
1Xinhua Hospital Affiliated to Shanghai Jiao Tong University School Of Medicine, shanghai, China

Keywords: Breast, Radiomics, Deep Learning

This study described the application of MRI-based deep learning radiomics in patients with breast cancer, presenting a novel individualized clinical decision nomogram that could be used to predict axillary lymph node metastasis providing a noninvasive approach to assist clinicians in clinical decision-making.