Literature DB >> 28011044

Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme.

Christopher J Lopez1, Natalya Nagornaya2, Nestor A Parra1, Deukwoo Kwon3, Fazilat Ishkanian1, Arnold M Markoe1, Andrew Maudsley2, Radka Stoyanova4.   

Abstract

PURPOSE: To build a framework for investigation of the associations between imaging, clinical target volumes (CTVs), and metabolic tumor volumes (MTVs) features for better understanding of the underlying information in the CTVs and dependencies between these volumes. High-throughput extraction of imaging and metabolomic quantitative features from magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging of glioblastoma multiforme (GBM) results in tens of variables per patient. In radiation therapy of GBM the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl aspartate (NAA) and choline (Cho). The corresponding clinical target volumes (CTVs) for radiation therapy are based on contrast-enhanced T1-weighted (CE-T1w) and T2-weighted (T2w)/fluid-attenuated inversion recovery MRI. METHODS AND MATERIALS: Necrotic portions, enhancing lesion, and edema were manually contoured on CE-T1w/T2w images for 17 GBM patients. Clinical target volumes and MTVs for NAA (MTVNAA) and Cho (MTVCho) were constructed. Imaging and metabolic features related to size, shape, and signal intensities of the volumes were extracted. Tumors were also scored categorically for 10 semantic imaging traits by a neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and CTVs/MTVs features were visualized as heatmaps. Associations between MTVNAA and MTVCho and imaging features were studied using Spearman correlation.
RESULTS: Forty-eight imaging features were extracted per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. Twenty features were extracted from CTVs and MTVs. A series of semantic imaging traits were replaced with automatically extracted continuous variables. There were multiple (22) significant correlations of imaging measures with CTVs/MTVNAA, whereas there were only 6 with CTVs/MTVCho.
CONCLUSIONS: A framework for investigation of codependencies between MRI and magnetic resonance spectroscopic imaging radiomic features and CTVs/MTVs has been established. The MTV for NAA was found to be closely associated with MRI volumes, whereas very few imaging features were related to MTVCho, indicating that Cho provides additional information to imaging.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 28011044      PMCID: PMC5730329          DOI: 10.1016/j.ijrobp.2016.11.011

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  34 in total

1.  Evaluation of peritumoral edema in the delineation of radiotherapy clinical target volumes for glioblastoma.

Authors:  Eric L Chang; Serap Akyurek; Tedde Avalos; Neal Rebueno; Chris Spicer; John Garcia; Robin Famiglietti; Pamela K Allen; K S Clifford Chao; Anita Mahajan; Shiao Y Woo; Moshe H Maor
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-02-15       Impact factor: 7.038

2.  Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients.

Authors:  James S Cordova; Hui-Kuo G Shu; Zhongxing Liang; Saumya S Gurbani; Lee A D Cooper; Chad A Holder; Jeffrey J Olson; Brad Kairdolf; Eduard Schreibmann; Stewart G Neill; Constantinos G Hadjipanayis; Hyunsuk Shim
Journal:  Neuro Oncol       Date:  2016-03-15       Impact factor: 12.300

3.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

Authors:  Yoganand Balagurunathan; Yuhua Gu; Hua Wang; Virendra Kumar; Olya Grove; Sam Hawkins; Jongphil Kim; Dmitry B Goldgof; Lawrence O Hall; Robert A Gatenby; Robert J Gillies
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

4.  Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas.

Authors:  Andreas Stadlbauer; Ewald Moser; Stephan Gruber; Rolf Buslei; Christopher Nimsky; Rudolf Fahlbusch; Oliver Ganslandt
Journal:  Neuroimage       Date:  2004-10       Impact factor: 6.556

5.  Patterns of failure following high-dose 3-D conformal radiotherapy for high-grade astrocytomas: a quantitative dosimetric study.

Authors:  S W Lee; B A Fraass; L H Marsh; K Herbort; S S Gebarski; M K Martel; E H Radany; A S Lichter; H M Sandler
Journal:  Int J Radiat Oncol Biol Phys       Date:  1999-01-01       Impact factor: 7.038

6.  Utility of multiparametric 3-T MRI for glioma characterization.

Authors:  Bhaswati Roy; Rakesh K Gupta; Andrew A Maudsley; Rishi Awasthi; Sulaiman Sheriff; Meng Gu; Nuzhat Husain; Sudipta Mohakud; Sanjay Behari; Chandra M Pandey; Ram K S Rathore; Daniel M Spielman; Jeffry R Alger
Journal:  Neuroradiology       Date:  2013-02-02       Impact factor: 2.804

7.  Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities.

Authors:  Haruka Itakura; Achal S Achrol; Lex A Mitchell; Joshua J Loya; Tiffany Liu; Erick M Westbroek; Abdullah H Feroze; Scott Rodriguez; Sebastian Echegaray; Tej D Azad; Kristen W Yeom; Sandy Napel; Daniel L Rubin; Steven D Chang; Griffith R Harsh; Olivier Gevaert
Journal:  Sci Transl Med       Date:  2015-09-02       Impact factor: 17.956

8.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.

Authors:  Maximilian Diehn; Christine Nardini; David S Wang; Susan McGovern; Mahesh Jayaraman; Yu Liang; Kenneth Aldape; Soonmee Cha; Michael D Kuo
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-24       Impact factor: 11.205

9.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

Authors:  David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

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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  11 in total

Review 1.  NCTN Assessment on Current Applications of Radiomics in Oncology.

Authors:  Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-01-31       Impact factor: 7.038

Review 2.  Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.

Authors:  Ji Eun Park; Ho Sung Kim
Journal:  Nucl Med Mol Imaging       Date:  2018-02-01

Review 3.  The biology and mathematical modelling of glioma invasion: a review.

Authors:  J C L Alfonso; K Talkenberger; M Seifert; B Klink; A Hawkins-Daarud; K R Swanson; H Hatzikirou; A Deutsch
Journal:  J R Soc Interface       Date:  2017-11       Impact factor: 4.118

4.  Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.

Authors:  Qingtao Qiu; Jinghao Duan; Zuyun Duan; Xiangjuan Meng; Changsheng Ma; Jian Zhu; Jie Lu; Tonghai Liu; Yong Yin
Journal:  Quant Imaging Med Surg       Date:  2019-03

5.  Heterogeneous parameters based on 18F-FET PET imaging can non-invasively predict tumor grade and isocitrate dehydrogenase gene 1 mutation in untreated gliomas.

Authors:  Tao Hua; Weiyan Zhou; Zhirui Zhou; Yihui Guan; Ming Li
Journal:  Quant Imaging Med Surg       Date:  2021-01

6.  Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Authors:  Edward Florez; Ali Fatemi; Pier Paolo Claudio; Candace M Howard
Journal:  SM J Clin Med Imaging       Date:  2018-03-15

7.  Re-irradiation of recurrent gliomas: pooled analysis and validation of an established prognostic score-report of the Radiation Oncology Group (ROG) of the German Cancer Consortium (DKTK).

Authors:  Stephanie E Combs; Maximilian Niyazi; Sebastian Adeberg; Nina Bougatf; David Kaul; Daniel F Fleischmann; Arne Gruen; Emmanouil Fokas; Claus M Rödel; Franziska Eckert; Frank Paulsen; Oliver Oehlke; Anca-Ligia Grosu; Annekatrin Seidlitz; Annika Lattermann; Mechthild Krause; Michael Baumann; Maja Guberina; Martin Stuschke; Volker Budach; Claus Belka; Jürgen Debus; Kerstin A Kessel
Journal:  Cancer Med       Date:  2018-03-23       Impact factor: 4.452

8.  Pseudoprogression of brain tumors.

Authors:  Stefanie C Thust; Martin J van den Bent; Marion Smits
Journal:  J Magn Reson Imaging       Date:  2018-05-07       Impact factor: 4.813

9.  Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer.

Authors:  Fangdie Ye; Yun Hu; Jiahao Gao; Yingchun Liang; Yufei Liu; Yuxi Ou; Zhang Cheng; Haowen Jiang
Journal:  Front Immunol       Date:  2021-10-18       Impact factor: 7.561

Review 10.  The Biological Meaning of Radiomic Features.

Authors:  Michal R Tomaszewski; Robert J Gillies
Journal:  Radiology       Date:  2021-01-05       Impact factor: 11.105

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