Literature DB >> 30925001

Translating preclinical MRI methods to clinical oncology.

David A Hormuth1,2, Anna G Sorace3,4,5,2,6,7,8, John Virostko4,5,2, Richard G Abramson9, Zaver M Bhujwalla10, Pedro Enriquez-Navas11, Robert Gillies11, John D Hazle12, Ralph P Mason13, C Chad Quarles14, Jared A Weis15, Jennifer G Whisenant16, Junzhong Xu17, Thomas E Yankeelov1,3,4,5,2.   

Abstract

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  CEST; MT; cancer; diffusion; elastography; perfusion

Year:  2019        PMID: 30925001      PMCID: PMC6766430          DOI: 10.1002/jmri.26731

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  107 in total

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Authors:  Mark Woods; Donald E Woessner; A Dean Sherry
Journal:  Chem Soc Rev       Date:  2006-05-10       Impact factor: 54.564

2.  Readout-segmented EPI for rapid high resolution diffusion imaging at 3 T.

Authors:  Samantha J Holdsworth; Stefan Skare; Rexford D Newbould; Raphael Guzmann; Nikolas H Blevins; Roland Bammer
Journal:  Eur J Radiol       Date:  2007-11-05       Impact factor: 3.528

Review 3.  Theranostics and metabolotheranostics for precision medicine in oncology.

Authors:  Zaver M Bhujwalla; Samata Kakkad; Zhihang Chen; Jiefu Jin; Sudath Hapuarachchige; Dmitri Artemov; Marie-France Penet
Journal:  J Magn Reson       Date:  2018-04-26       Impact factor: 2.229

Review 4.  On high b diffusion imaging in the human brain: ruminations and experimental insights.

Authors:  Robert V Mulkern; Steven J Haker; Stephan E Maier
Journal:  Magn Reson Imaging       Date:  2009-06-10       Impact factor: 2.546

5.  Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI.

Authors:  Christiane K Kuhl; Simone Schrading; Kevin Strobel; Hans H Schild; Ralf-Dieter Hilgers; Heribert B Bieling
Journal:  J Clin Oncol       Date:  2014-06-23       Impact factor: 44.544

6.  Administration and (1)H MRS detection of histidine in human brain: application to in vivo pH measurement.

Authors:  P Vermathen; A A Capizzano; A A Maudsley
Journal:  Magn Reson Med       Date:  2000-05       Impact factor: 4.668

7.  Combined vascular and extracellular pH imaging of solid tumors.

Authors:  Z M Bhujwalla; D Artemov; P Ballesteros; S Cerdan; R J Gillies; M Solaiyappan
Journal:  NMR Biomed       Date:  2002-04       Impact factor: 4.044

8.  Monitoring of treatment response after chemoradiotherapy for head and neck cancer using in vivo 1H MR spectroscopy.

Authors:  Ann D King; David K W Yeung; Kwok-Hung Yu; Frankie K F Mo; Chen-Wen Hu; Kunwar S Bhatia; Gary M K Tse; Alexander C Vlantis; Jeffrey K T Wong; Anil T Ahuja
Journal:  Eur Radiol       Date:  2009-08-05       Impact factor: 5.315

9.  THE METABOLISM OF TUMORS IN THE BODY.

Authors:  O Warburg; F Wind; E Negelein
Journal:  J Gen Physiol       Date:  1927-03-07       Impact factor: 4.086

10.  Comparison of three reference methods for the measurement of intracellular pH using 31P MRS in healthy volunteers and patients with lymphoma.

Authors:  Mihaela Rata; Sharon L Giles; Nandita M deSouza; Martin O Leach; Geoffrey S Payne
Journal:  NMR Biomed       Date:  2014-02       Impact factor: 4.044

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  12 in total

Review 1.  Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Authors:  Chengyue Wu; Guillermo Lorenzo; David A Hormuth; Ernesto A B F Lima; Kalina P Slavkova; Julie C DiCarlo; John Virostko; Caleb M Phillips; Debra Patt; Caroline Chung; Thomas E Yankeelov
Journal:  Biophys Rev (Melville)       Date:  2022-05-17

Review 2.  Choosing The Right Animal Model for Renal Cancer Research.

Authors:  Paweł Sobczuk; Anna Brodziak; Mohammed Imran Khan; Stuti Chhabra; Michał Fiedorowicz; Marlena Wełniak-Kamińska; Kamil Synoradzki; Ewa Bartnik; Agnieszka Cudnoch-Jędrzejewska; Anna M Czarnecka
Journal:  Transl Oncol       Date:  2020-02-22       Impact factor: 4.243

3.  Math, magnets, and medicine: enabling personalized oncology.

Authors:  David A Hormuth; Angela M Jarrett; Guillermo Lorenzo; Ernesto A B F Lima; Chengyue Wu; Caroline Chung; Debra Patt; Thomas E Yankeelov
Journal:  Expert Rev Precis Med Drug Dev       Date:  2021-01-27

Review 4.  Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors.

Authors:  Li Liu; Devin O'Kelly; Regan Schuetze; Graham Carlson; Heling Zhou; Mary Lynn Trawick; Kevin G Pinney; Ralph P Mason
Journal:  Molecules       Date:  2021-04-27       Impact factor: 4.411

Review 5.  Preclinical Applications of Multi-Platform Imaging in Animal Models of Cancer.

Authors:  Natalie J Serkova; Kristine Glunde; Chad R Haney; Mohammed Farhoud; Alexandra De Lille; Elizabeth F Redente; Dmitri Simberg; David C Westerly; Lynn Griffin; Ralph P Mason
Journal:  Cancer Res       Date:  2020-12-01       Impact factor: 13.312

Review 6.  Magnetic Resonance Imaging for Translational Research in Oncology.

Authors:  Maria Felicia Fiordelisi; Carlo Cavaliere; Luigi Auletta; Luca Basso; Marco Salvatore
Journal:  J Clin Med       Date:  2019-11-06       Impact factor: 4.241

7.  An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.

Authors:  Wilfred W Lam; Wendy Oakden; Elham Karami; Margaret M Koletar; Leedan Murray; Stanley K Liu; Ali Sadeghi-Naini; Greg J Stanisz
Journal:  Sci Rep       Date:  2020-05-15       Impact factor: 4.379

8.  Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.

Authors:  Kooresh I Shoghi; Cristian T Badea; Stephanie J Blocker; Thomas L Chenevert; Richard Laforest; Michael T Lewis; Gary D Luker; H Charles Manning; Daniel S Marcus; Yvonne M Mowery; Stephen Pickup; Ann Richmond; Brian D Ross; Anna E Vilgelm; Thomas E Yankeelov; Rong Zhou
Journal:  Tomography       Date:  2020-09

Review 9.  Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities.

Authors:  Angela M Jarrett; Danial Faghihi; David A Hormuth Ii; Ernesto A B F Lima; John Virostko; George Biros; Debra Patt; Thomas E Yankeelov
Journal:  J Clin Med       Date:  2020-05-02       Impact factor: 4.241

10.  Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling.

Authors:  David A Hormuth; Angela M Jarrett; Thomas E Yankeelov
Journal:  Radiat Oncol       Date:  2020-01-02       Impact factor: 3.481

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