Kyle Kalutkiewicz¹, Jiahui Li¹, Zheng Zhu¹, Jun Chen¹, Taofic Mounajjed², Kevin J. Glaser¹, Safa Hoodeshenas¹, Sudhakar K. Venkatesh¹, Armando Manduca¹, Claude B. Sirlin³, Vijay H. Shah⁴, Alina M. Allen⁴, Richard L. Ehman¹, and Meng Yin^1
1Radiology, Mayo Clinic, Rochester, MN, United States, ²Surgical Pathology, Mayo Clinic, Rochester, MN, United States, ³Radiology, UC San Diego, La Jolla, CA, United States, ⁴Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States

Liver biopsy and multi-parametric MRI/MRE were performed on 380 patients at risk of NASH. Histopathological diagnoses of NASH were collected along with proton density fat fraction (PDFF), and 3D-MRE measured liver stiffness (LS) and loss modulus (LM). While PDFF alone showed good ability to predict NASH (AUC: 0.80 [0.76, 0.85]), the addition of MRE measurements increased the diagnostic performance with strong statistical significance (AUC: 0.85 [0.81, 0.89], p<0.0004). LS alone was also found to be the strongest predictor of NASH-required treatment (AUC: 0.89 [0.84, 0.93], p<0.05), further establishing the critical role of MRE for clinical management of liver disease.

Vadim Malis¹, Won Bae¹, Jirach Kungsamutr², Xiaowei Zhang¹, Yoshimori Kassai³, Andrew Yen¹, Susan Hopkins¹, Yoshiharu Ohno⁴, and Mitsue Miyazaki^1
1Radiology, UC San Diego, San Diego, CA, United States, ²Bioengineering, UC San Diego, San Diego, CA, United States, ³Canon Medical Systems Corp., Tochigi, Japan, ⁴Radiology, Fujita Health University, Toyoake, Japan

Free-breathing 3D MRI techniques are applied to explore the impact of COVID-19 in adults. Single echo non-fat suppressed Ultra-short TE (UTE) was utilized for automatic segmentation of lung, Multi-echo UTE with fat suppression was used to obtain T₂* maps, and UTE with Time-Spatial Labeling Inversion Pulse (Time-SLIP) allowed to visualize pulmonary vasculature.

Arvin Arani¹, Jessica Magnuson¹, Zheng Zhu¹, Joshua D. Trzasko¹, Yi Sui¹, Devanshi Damani¹, Kevin Glaser¹, Matthew C. Murphy¹, Meng Yin¹, Angela Dispenzieri¹, Richard L. Ehman¹, Sudhakar K. Venkatesh¹, and Philip A. Araoz^1
1Mayo Clinic, Rochester, MN, United States

Light chain (AL) cardiac amyloidosis is a disease where abnormal proteins are deposited in multiple organs and is associated with elevated tissue stiffness. In cases with poor prognosis, autologous hematopoietic stem cell transplantation (ASCT) is used for therapy. Organ response monitoring is currently limited and challenging.  The objective of this study was to evaluate the feasibility of using magnetic resonance elastography (MRE) to monitor stiffness changes in multiple organs pre- and post- ASCT therapy. At 3 months post therapy, significant reduction in MRE stiffness (p<0.001) was observed in organs that exhibited successful therapy response (defined by 9 month clinical outcomes).

Milica Medved¹, Hunter D. D Witmer², Ankit D. D Dhiman², Yaniv Berger³, Scott K Sherman⁴, Enal S Hindi², Samuel G Armato III¹, Ingrid S Reiser¹, Aytekin Oto¹, Roger M Engelmann¹, Hedy L Kindler⁵, Nisa C Oren¹, Carla B Harmath¹, and Kiran K Turaga^2
1Department of Radiology, The University of Chicago, Chicago, IL, United States, ²Department of Surgery, The University of Chicago, Chicago, IL, United States, ³Department of Surgery B, Chaim Sheba Medical Center, Ramat Gan, Israel, ⁴Department of Surgery, University of Iowa, Iowa City, IA, United States, ⁵Department of Medicine, The University of Chicago, Chicago, IL, United States

Improved methods for visualizing malignant peritoneal mesothelioma are needed.  A high-resolution, high-contrast MRI protocol at 3T, including multiple delayed phases, was tested in 18 patients scheduled for laparoscopy/laparotomy.  Both contrast washout and ongoing uptake was observed in individual lesions, pointing to a need for imaging with both short and long post-contrast delay times for optimal sensitivity.  The per-patient average uptake/washout rate was significantly correlated with the cumulative nuclear score on pathology.  The tested MRI protocol is feasible and may allow for more accurate visualization of peritoneal lesions and better guidance of management options.

Jong Bum Son¹, Ken-Pin Hwang¹, David E Rauch¹, Ju Hee Ahn², Jiyoung Lee³, Zijian Zhou¹, Bikash Panthi⁴, Beatriz Adrada⁴, Rosalind P Candelaria⁴, Jason B White⁵, Mary Guirguis⁴, Rania M Mohamed⁶, Elizabeth E Ravenberg⁷, Clinton Yam⁷, Debasish Tripathy⁷, and Jingfei Ma^1
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Radiology Department, GangNam Radiology Clinic, Busan, Korea, Republic of, ³College of Medicine, Chung-Ang University, Seongnam-si, Korea, Republic of, ⁴Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Department of Moon Shots Operations, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁶Department of Breast Imaging Research, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁷Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

We developed a hybrid deep learning network combining convolutional neural (CNN) and long short-term memory (LTSM) networks to predict slice-to-slice consistent responses to neoadjuvant systematic therapy (NAST) in triple negative breast cancer (TNBC) patients using multislice quantitative SyntheticMR images. We demonstrated that neural networks originally developed for video feature classification can be adapted to predict treatment response of cancer patients using MR images. Our hybrid network was able to overcome the slice-to-slice inconsistency that would have resulted if a 2D network is applied directly, therefore providing higher prediction accuracy.

Paola Porcari¹, Ellen Ackerstaff¹, Soe Su Min¹, Dov P. Winkleman¹, H. Carl Lekaye¹, and Jason A. Koutcher^2,3,4,5
1Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, United States, ²Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ⁴Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ⁵Weill Cornell Medical College, Cornell University, New York, NY, United States

 We investigated the effect of Deferiprone, an iron chelator clinically used for non-cancer related diseases, on cellular metabolism and the impairment of cell growth in the human MDA-MB-231 and murine 4T1 triple-negative breast cancer models. By comparing the findings obtained on both cell lines through ¹³C MRS monitoring with those related to mitochondrial respiration, we aimed to better understand the metabolic mechanism driving the changes that follow Deferiprone exposure. A stronger effect of DFP exposure was observed in human MDA-MB-231 cells than murine 4T1 cells. 

Ravneet Vohra¹, Yak-Nam Wang², Helena Son³, Stephanie Totten², Akshit Arora⁴, Adam Maxwell⁵, and Donghoon Lee^1
1Radiology, University of Washington, Seattle, WA, United States, ²Applied Physics Laboratory, University of Washington, Seattle, WA, United States, ³Gastroenterology, University of Washington, Seattle, WA, United States, ⁴Arts and Sciences, University of Washington, Seattle, WA, United States, ⁵Urology, University of Washington, Seattle, WA, United States

Pancreatic cancer is expected to become the second leading cause of the cancer-related deaths in the USA by 2030. A genetically engineered mouse model (KPC) offers an alternative to transplantation models for preclinical therapeutic evaluation as it expresses mutations similar to human pancreatic cells. MRI can be used to monitor the tumor microenvironment in a variety of tumors. The goal of this study was to monitor fibrotic tissue and hyaluronan deposition in the KPC mouse model during the late stages of tumor development.