Literature DB >> 25421725

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome.

James P B O'Connor1, Chris J Rose2, John C Waterton3, Richard A D Carano4, Geoff J M Parker2, Alan Jackson2.   

Abstract

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. ©2014 American Association for Cancer Research.

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Year:  2014        PMID: 25421725      PMCID: PMC4688961          DOI: 10.1158/1078-0432.CCR-14-0990

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  81 in total

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3.  Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

Authors:  Weijie Chen; Maryellen L Giger; Hui Li; Ulrich Bick; Gillian M Newstead
Journal:  Magn Reson Med       Date:  2007-09       Impact factor: 4.668

4.  BI-RADS categorization as a predictor of malignancy.

Authors:  S G Orel; N Kay; C Reynolds; D C Sullivan
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5.  Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Part I.

Authors:  Erica C Henning; Chieko Azuma; Christopher H Sotak; Karl G Helmer
Journal:  Magn Reson Med       Date:  2007-03       Impact factor: 4.668

6.  The positive predictive value of mammographic signs: a review of 425 non-palpable breast lesions.

Authors:  H C Burrell; S E Pinder; A R Wilson; A J Evans; L J Yeoman; C W Elston; I O Ellis
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7.  Comparison of 18F-FDG and 11C-methionine for PET-guided stereotactic brain biopsy of gliomas.

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Review 8.  Intratumor heterogeneity: evolution through space and time.

Authors:  Charles Swanton
Journal:  Cancer Res       Date:  2012-09-20       Impact factor: 12.701

Review 9.  Improving tumour heterogeneity MRI assessment with histograms.

Authors:  N Just
Journal:  Br J Cancer       Date:  2014-09-30       Impact factor: 7.640

10.  Use of dynamic contrast-enhanced MRI to evaluate acute treatment with ZD6474, a VEGF signalling inhibitor, in PC-3 prostate tumours.

Authors:  D Checkley; J J Tessier; J Kendrew; J C Waterton; S R Wedge
Journal:  Br J Cancer       Date:  2003-11-17       Impact factor: 7.640

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  201 in total

1.  Association Between Computed Tomographic Features and Kirsten Rat Sarcoma Viral Oncogene Mutations in Patients With Stage I Lung Adenocarcinoma and Their Prognostic Value.

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Journal:  Clin Lung Cancer       Date:  2015-11-12       Impact factor: 4.785

Review 2.  Image-based biomarkers for solid tumor quantification.

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Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

3.  Perspectives in Radiomics for Personalized Medicine and Theranostics.

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Review 4.  Acidosis and cancer: from mechanism to neutralization.

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Journal:  Cancer Metastasis Rev       Date:  2019-06       Impact factor: 9.264

5.  Contrast-enhanced 3T MR Perfusion of Musculoskeletal Tumours: T1 Value Heterogeneity Assessment and Evaluation of the Influence of T1 Estimation Methods on Quantitative Parameters.

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Journal:  Eur Radiol       Date:  2017-06-14       Impact factor: 5.315

6.  Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.

Authors:  SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff
Journal:  Magn Reson Med       Date:  2017-07-20       Impact factor: 4.668

7.  Three-dimensional alginate hydrogels for radiobiological and metabolic studies of cancer cells.

Authors:  Graham H Read; Natsuko Miura; Jenna L Carter; Kelsey T Kines; Kazutoshi Yamamoto; Nallathamby Devasahayam; Jason Y Cheng; Kevin A Camphausen; Murali C Krishna; Aparna H Kesarwala
Journal:  Colloids Surf B Biointerfaces       Date:  2018-06-18       Impact factor: 5.268

Review 8.  Review of quantitative multiscale imaging of breast cancer.

Authors:  Michael A Pinkert; Lonie R Salkowski; Patricia J Keely; Timothy J Hall; Walter F Block; Kevin W Eliceiri
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-22

Review 9.  Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI.

Authors:  James P B O'Connor; Simon P Robinson; John C Waterton
Journal:  Br J Radiol       Date:  2019-01-24       Impact factor: 3.039

10.  Monitoring the Vascular Response and Resistance to Sunitinib in Renal Cell Carcinoma In Vivo with Susceptibility Contrast MRI.

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Journal:  Cancer Res       Date:  2017-05-31       Impact factor: 12.701

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