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Keywords: Breast, Breast, Abbreviated Breast MRI

We report on the results of a prospective screening trial using abbreviated breast MRI with ultrafast dynamic contrast-enhanced imaging (3.5 seconds per time point) to screen for breast cancer in 166 women at any risk of developing breast cancer. The cancer detection rate was 24.1 per 1000 women while maintaining a specificity of 97.6%. The biopsy recommendation and abnormal interpretation rates were lower than in other published trials that did not include ultrafast imaging in their abbreviated protocol.

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Keywords: Breast, Breast, Artificial Intelligence

 Contrast-enhanced breast MRI exams typically last more than 20 minutes even though the dynamic contrast-enhanced sequences alone would be sufficient for interpretation in over 95% of cases. Thus, we present a personalized on-the-fly MRI protocoling paradigm, where a deep learning algorithm uses images acquired during the first few minutes of the exam to triage patients to an abbreviated versus full MRI protocol. We conduct a retrospective reader study to show that this personalized scanning paradigm decreases scan time and cost while maintaining diagnostic performance.

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Lena Nohava¹, Michael Obermann¹, and Elmar Laistler^1
1High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria

Keywords: Breast, Visualization

A wearable coil worn like a vest (“BraCoil”) has recently been shown to improve signal-to-noise ratio by a factor of up to three in supine breast MRI. However, the visualization of supine breast images is inefficient when using Cartesian views due to the bent shape of the breast along the chest wall. We introduce a panoramic view similar to panoramic dental X-ray to make reading of supine breast MRI more efficient by reducing the number of slices by a factor of 2-4, depending on breast size.

Bingyu Yao^1,2, Zhou Liu³, Yumin Chen², Jie Wen³, Meng Wang³, Ya Ren³, Dong Liang¹, Xin Liu¹, Hairong Zheng¹, Dehong Luo³, and Na Zhang^1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ³Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

Keywords: Breast, Cancer

We propose a novel model-free and data-driven approach, i.e., voxel-wise composition ratio on 19 dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) profiles (Type-19) to visualize and quantify spatial hemodynamic heterogeneity. The proposed quantitative method for breast tumor was evaluated and compared with the two existing methods (qualitative and semi-quantitative methods) in 4 different clinical applications. In distinguishing malignancy on breast cancer lesions and predicting tumor proliferation status, we found that the machine learning model based on the Type-19 feature outperformed other two models in the validation set. 

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Rachelle Crescenzi^1,2, Paula M.C. Donahue^3,4, R. Sky Jones⁵, Chelsea Lee⁶, Maria Garza^1,5, Niral J Patel⁶, Ingrid Meszoely⁷, and Manus J Donahue^5,8
1Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ²Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ³Physical Medicine and Rehabilitation, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Dayani Center for Health and Wellness, Vanderbilt University Medical Center, Nashville, TN, United States, ⁵Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁶Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁷Department of Surgical Oncology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁸Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States

Keywords: Breast, CEST & MT, cancer, lymph node, metastasis, breast cancer

The overall goal of this work is to apply a CEST-Dixon MRI approach in the axillary lymph nodes (LNs) of women with breast cancer to test fundamental hypotheses about biochemical LN profiles with carcinoma. Mean z-spectra and corresponding significant differences in cohorts with metastatic vs. benign LNs were observed in regions of known chemical exchange for the nuclear Overhauser effect (PTR=0.087 vs. 0.051, p=0.04), hydroxyl (PTR=0.272 vs. 0.212, p=0.003), and amine (PTR=0.081 vs. 0.033, p=0.003) protons. CEST-Dixon MRI of LNs may have relevance for pre-surgical breast cancer staging and for guiding LN sparing resections to reduce risk for lymphedema.

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Christof Boehm¹, Alexander Komenda¹, Kilian Weiss², Jonathan K. Stelter¹, Tabea Borde¹, Jakob Meineke³, Marcus R. Makowski¹, Eva M. Fallenberg¹, and Dimitrios C. Karampinos^1
1Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ²Philips GmbH Market DACH, Hamburg, Germany, ³Philips Research, Hamburg, Germany

Keywords: Breast, Susceptibility

Breast microcalcifications (MCs) can be the only sign of carcinoma and other precursor lesions of breast cancer in x-ray mammography. Mammography is routinely used for population-based breast cancer screening. However, due to the ionizing nature of x-ray radiation, the use of an MR-based technique would be desirable for repeated examinations especially in screening targeted sub-cohorts at high-risk or younger women. In this work, we present initial results on the visualization of MCs using MR with an optimized high-resolution GRE-scan and quantitative susceptibility mapping. The proposed methodology allows for the robust visualization of MC clusters in MR for the first time.

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Martins Otikovs¹, Noam Nissan², Edna Furman-Haran¹, Debbie Anaby², Ravit Agassi³, Miri Sklair-Levy^2,4, and Lucio Frydman^1
1Weizmann Institute of Science, Rehovot, Israel, ²Sheba Medical Center, Ramat Gan, Israel, ³Ben Gurion University Hospital, Beer Sheva, Israel, ⁴Sackler School of Medicine, Tel Aviv, Israel

Keywords: Breast, Cancer

The possibility to improve the MRI contrast on cancerous breast lesions was explored by correlating quantitative T2 and ADC values. To this end, T2 maps of patients were measured at more than one b-value and ADC maps at more than one TE value –in all cases using spatiotemporal encoding (SPEN) MRI. SPEN delivered quality, artifact-free TE-weighted DW images, from which ADC and T2 maps could be correlated despite relatively long acquisitions and heavy signal weighting. Data suggest there may be potential value in combining T2 and ADC mappings, as these low-correlated variables can provide complementary information about breast lesions.

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Soo-Yeon Kim¹, Jun-Hyung Kim ², Nariya Cho¹, and Dong-Hyun Kim^2
1Radiology, Seoul National University Hospital, Seoul, Korea, Republic of, ²Electrical and Electronic Engineering, Yonsei university, Seoul, Korea, Republic of

Keywords: Breast, Electromagnetic Tissue Properties

We evaluated the role of MRI as a problem-solving tool to reduce false-positive biopsy caused by breast ultrasound. Seventy nine participants underwent MRI prior to ultrasound-guided core needle biopsy. The MRI protocol consisted of T2-weighted imaging for conductivity map reconstruction, diffusion-weighted imaging, and abbreviated contrast-enhanced MRI. The conductivity, ADC, and BI-RADS criterion lowered false-positive biopsy by 23%, 38%, and 43%, respectively, while being able to detect all cancers. Conductivity imaging showed the potential but showed lower performance than diffusion-weighted imaging and abbreviated MRI in reducing unnecessary biopsies caused by breast ultrasound.

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Rena Nakayama¹, Mami Iima^2,3, Masako Kataoka², Maya Honda^2,4, Yuta Urushibata⁵, Martins Otikovs⁶, Noam Nissan⁷, Lucio Frydman⁶, Rie Ota^2,8, Aika Okazawa², Kazuki Tsuji¹, Masakazu Toi⁹, and Yuji Nakamoto^2
1Kyoto University Faculty of Medicine, Kyoto, Japan, ²Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, ³Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan, ⁴Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan, ⁵Siemens Healthcare K.K., Tokyo, Japan, ⁶Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel, ⁷Radiology, Sheba-Medical-Center, Ramat-Gan, Israel, ⁸Tenri Hospital, Nara, Japan, ⁹Breast Surgery, Kyoto University Hospital, Kyoto, Japan

Keywords: Breast, Breast

This study investigated the breast lesion conspicuity and ADC reliability for 3 different DWI protocols; SPEN, SS-EPI, and RESOLVE. The in-plane resolution for SPEN and RESOLVE was 1x1mm², and SS-EPI was 2x2mm². SPEN showed a higher score for lesion conspicuity than SS-EPI, and a lower score than RESOLVE.  ADC values in breast lesions were significantly lower in SPEN than others, presumably due to the choice of different b values, the sequences having different T1/T2 weightings, different robustness vs motions,   the presence of unsuppressed fat, or  different viewer systems used to analyze the data, which will need further investigation. 

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Nu N Le¹, David C Newitt¹, Wen Li¹, Deep Hathi¹, Jiachao Liang¹, Lisa J Wilmes¹, Jessica E Gibbs¹, Natsuko Onishi¹, Bonnie N Joe¹, John Kornak², Patrick J Bolan³, Savannah C Partridge⁴, and Nola M Hylton^1
1Radiology, University of California, San Francisco, San Francisco, CA, United States, ²Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, United States, ³Radiology, University of Minnesota, Minneapolis, MN, United States, ⁴Radiology, University of Washington, Seattle, WA, United States

Keywords: Breast, Cancer

Diffusion-weighted imaging (DWI) in MRI plays an important role in diagnostic applications. Automated tumor segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed for dynamic contrast enhanced (DCE) MRI, automatic segmentation in breast DWI-MRI is still underdeveloped. The purpose of this study is to develop methods to transfer functional tumor volume (FTV) analysis ROI from DCE-MRI image space to DWI-MRI image space using image registration and extract ADC statistics in regions corresponding to those of DCE segmentations for prediction of pathologic treatment response (pCR).

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Zhong-Wei Chen¹, You-Fan Zhao¹, Hui-Ru Liu¹, Jie-Jie Zhou¹, Hai-Wei Miao¹, Shu-Xin Ye¹, Yun He¹, Xin-Miao Liu¹, Min-Ying Su^2,3, and Mei-Hao Wang^1
1the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China, ²University of California, Irvine, CA, United States, ³Kaohsiung Medical University, Kaohsiung, Taiwan

Keywords: Breast, Breast

How ADC could be combined with Kaiser score (KS) for the diagnosis of breast cancer was emerged as an interesting research area. We modified KS to KS+ based on the dichotomized ADC >1.4×10^-3 mm²/s, and integrated KS and the continuous ADC values to build machine learning (ML) models for assessment. The diagnostic specificity of KS+ was higher than that of KS with a slightly degraded sensitivity. The AUCs of them were not significantly different. When the KS and the continuous ADC values were used to train ML models, the performance could be further improved while maintaining at a high sensitivity.

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Dibash Basukala¹, Artem Mikheev¹, Nima Gilani¹, Thomas Benkert², Linda Moy¹, Katja Pinker-Domenig³, Sunitha B. Thakur³, and Eric E. Sigmund^1
1Radiology, NYU Langone Health, New York, NY, United States, ²MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ³Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Keywords: Breast, Phantoms, IVIM, Reproducibility

Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging (DWI) is helpful in the characterization of breast tumors. Toward this goal, a novel breast phantom containing tubes of different polyvinylpyrrolidone (PVP) concentrations, water, fat, and sponge flow chambers was utilized.  This work tests this breast phantom at two sites employing different vendor MRI scanners to estimate the ADC and IVIM parameters. The results are reproducible within sites, and show progress towards reproducibility across sites and vendors, and can be used in the future in multicenter clinical trials for breast cancer characterization, prediction and prognosis.

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Shuhao Shi¹, Lu Wang¹, Jianfeng Bao², Zhigang Wu³, Congbo Cai¹, Zhong Chen¹, Jiazheng Wang³, and Shuhui Cai^1
1Department of Electronic Science, Xiamen University, Xiamen, China, ²Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ³MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Keywords: Breast, Cancer

Intravoxel incoherent motion (IVIM) with multiple b-values, as an advanced diffusion model, provides accurate identification of breast cancer. However, the IVIM-derived parameters vary greatly depending on different fitting methods, especially for parameters D^* and f. In this study, we proposed a method for high-quality breast IVIM reconstruction based on deep neural network. Data analysis shows that our proposed method improves the visual quality of breast IVIM parametric maps with better benign and malignant breast lesion differentiation ability compared to the traditional least-square fitting method.

Wenjie Tang¹, Yuan Guo¹, Siyi Chen¹, Xueli Li¹, Yongzhou Xu², and Xinhua Wei^1
1Guangzhou First People's Hospital, Guangzhou, China, ²Philips Healthcare, Guangzhou, China

Keywords: Breast, Cancer, Breast neoplasms, Neoadjuvant chemotherapy, Radiologists

To compare the diagnostic efficacy of abbreviated breast MRI (AB-MRI) and regular full diagnostic protocol MRI (FDP-MRI) in the assessment of response to neoadjuvant chemotherapy (NAC) in breast cancer, this study retrospectively analyzed the performance of AB-MRI and FDP-MRI in breast cancer patients undergoing NAC. We found that, for experienced radiologists, compared with FDP-MRI, AB-MRI showed similar sensitivity, specificity, and accuracy in the evaluation of pCR and similar accuracy in the measurement of residual tumor size for breast cancer.

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Sai Man Cheung¹, Wing Shan Wu¹, Nicholas Senn¹, Ravi Sharma², Trevor McGoldrick², Tanja Gagliardi^1,3, Ehab Husain⁴, Yazan Masannat⁵, and Jiabao He^1,6
1Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom, ²Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, ³Department of Radiology, Royal Marsden Hospital, London, United Kingdom, ⁴Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, ⁵Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, ⁶Translational and Clinical Research Institute, Newcastle University, Aberdeen, United Kingdom

Keywords: Breast, MR-Guided Interventions

Breast cancer is a major and expanding health challenge, and neoadjuvant chemotherapy (NACT) is increasingly prescribed to facilitate breast surgery. However, response to NACT is highly inconsistent, imposing an ongoing demand for improved imaging methods for early response identification. Tissue perfusion, a sensitive marker of cancer metabolism, can be derived from intravoxel incoherent motion (IVIM) model, and recent Bayesian algorithm yields increased sensitivity and precision in pancreatic cancer. We therefore hypothesise that IVIM model powered by Bayesian algorithm is able to detect early treatment-induced changes in tumour perfusion and diffusion, with the potential to impact patient care pathway.