Literature DB >> 27729326

PET and MRI: Is the Whole Greater than the Sum of Its Parts?

Robert J Gillies1,2, Thomas Beyer3.   

Abstract

Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems, appropriate for both preclinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz and colleagues, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data. With combined PET and MRI and sophisticated machine-learning tools, they have been able identify multiple coexisting regions ("habitats") within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm-shifting applications, it is not the last word on the subject and introduces a number of new avenues of investigation to pursue. Cancer Res; 76(21); 6163-6. ©2016 AACR. ©2016 American Association for Cancer Research.

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Year:  2016        PMID: 27729326      PMCID: PMC5408508          DOI: 10.1158/0008-5472.CAN-16-2121

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  28 in total

1.  "Anatometabolic" tumor imaging: fusion of FDG PET with CT or MRI to localize foci of increased activity.

Authors:  R L Wahl; L E Quint; R D Cieslak; A M Aisen; R A Koeppe; C R Meyer
Journal:  J Nucl Med       Date:  1993-07       Impact factor: 10.057

Review 2.  Quantitative imaging in cancer evolution and ecology.

Authors:  Robert A Gatenby; Olya Grove; Robert J Gillies
Journal:  Radiology       Date:  2013-10       Impact factor: 11.105

Review 3.  Multiparametric Magnetic Resonance Imaging in the Diagnosis of Prostate Cancer: A Systematic Review.

Authors:  M A Haider; X Yao; A Loblaw; A Finelli
Journal:  Clin Oncol (R Coll Radiol)       Date:  2016-05-30       Impact factor: 4.126

Review 4.  Magnetic resonance imaging of prostate cancer.

Authors:  Serkan Guneyli; Cemile Zuhal Erdem; Lutfi Oktay Erdem
Journal:  Clin Imaging       Date:  2016-02-16       Impact factor: 1.605

5.  Combined PET/MR: The Real Work Has Just Started. Summary Report of the Third International Workshop on PET/MR Imaging; February 17-21, 2014, Tübingen, Germany.

Authors:  D L Bailey; G Antoch; P Bartenstein; H Barthel; A J Beer; S Bisdas; D A Bluemke; R Boellaard; C D Claussen; C Franzius; M Hacker; H Hricak; C la Fougère; B Gückel; S G Nekolla; B J Pichler; S Purz; H H Quick; O Sabri; B Sattler; J Schäfer; H Schmidt; J van den Hoff; S Voss; W Weber; H F Wehrl; T Beyer
Journal:  Mol Imaging Biol       Date:  2015-06       Impact factor: 3.488

6.  Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study.

Authors:  Jennifer Schmitz; Julian Schwab; Johannes Schwenck; Qian Chen; Leticia Quintanilla-Martinez; Markus Hahn; Beate Wietek; Nina Schwenzer; Annette Staebler; Ursula Kohlhofer; Olulanu H Aina; Neil E Hubbard; Gerald Reischl; Alexander D Borowsky; Sara Brucker; Konstantin Nikolaou; Christian la Fougère; Robert D Cardiff; Bernd J Pichler; Andreas M Schmid
Journal:  Cancer Res       Date:  2016-07-27       Impact factor: 12.701

7.  The future of hybrid imaging-part 2: PET/CT.

Authors:  Thomas Beyer; David W Townsend; Johannes Czernin; Lutz S Freudenberg
Journal:  Insights Imaging       Date:  2011-02-20

8.  Assessment of Lymph Nodes and Prostate Status Using Early Dynamic Curves with (18)F-Choline PET/CT in Prostate Cancer.

Authors:  Cédric Mathieu; Ludovic Ferrer; Thomas Carlier; Mathilde Colombié; Daniela Rusu; Françoise Kraeber-Bodéré; Loic Campion; Caroline Rousseau
Journal:  Front Med (Lausanne)       Date:  2015-09-09

9.  PET/CT in Oncology: Current Status and Perspectives.

Authors:  Johannes Czernin; Martin Allen-Auerbach; David Nathanson; Ken Herrmann
Journal:  Curr Radiol Rep       Date:  2013-05-03

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  5 in total

Review 1.  Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.

Authors:  Aleksandra Karolak; Dmitry A Markov; Lisa J McCawley; Katarzyna A Rejniak
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

2.  Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.

Authors:  Bruna V Jardim-Perassi; Suning Huang; William Dominguez-Viqueira; Jan Poleszczuk; Mikalai M Budzevich; Mahmoud A Abdalah; Smitha R Pillai; Epifanio Ruiz; Marilyn M Bui; Debora A P C Zuccari; Robert J Gillies; Gary V Martinez
Journal:  Cancer Res       Date:  2019-06-11       Impact factor: 12.701

3.  Molecular Imaging of the Tumor Microenvironment Reveals the Relationship between Tumor Oxygenation, Glucose Uptake, and Glycolysis in Pancreatic Ductal Adenocarcinoma.

Authors:  Kazutoshi Yamamoto; Jeffrey R Brender; Tomohiro Seki; Shun Kishimoto; Nobu Oshima; Rajani Choudhuri; Stephen S Adler; Elaine M Jagoda; Keita Saito; Nallathamby Devasahayam; Peter L Choyke; James B Mitchell; Murali C Krishna
Journal:  Cancer Res       Date:  2020-04-03       Impact factor: 12.701

4.  Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients.

Authors:  Barbara M Fischer; Flemming L Andersen; Anders B Olin; Adam E Hansen; Jacob H Rasmussen; Björn Jakoby; Anne K Berthelsen; Claes N Ladefoged; Andreas Kjær
Journal:  EJNMMI Phys       Date:  2022-03-16

Review 5.  Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.

Authors:  Sandy Napel; Wei Mu; Bruna V Jardim-Perassi; Hugo J W L Aerts; Robert J Gillies
Journal:  Cancer       Date:  2018-11-01       Impact factor: 6.860

  5 in total

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