Literature DB >> 26835490

Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation.

Joonsang Lee1, Shivali Narang1, Juan J Martinez2, Ganesh Rao2, Arvind Rao1.   

Abstract

We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.

Entities:  

Keywords:  glioblastoma; imaging-genomics; radiomics; spatial diversity; tumor habitats

Year:  2015        PMID: 26835490      PMCID: PMC4718420          DOI: 10.1117/1.JMI.2.4.041006

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  30 in total

1.  Distilling free-form natural laws from experimental data.

Authors:  Michael Schmidt; Hod Lipson
Journal:  Science       Date:  2009-04-03       Impact factor: 47.728

2.  Mig-6 controls EGFR trafficking and suppresses gliomagenesis.

Authors:  Haoqiang Ying; Hongwu Zheng; Kenneth Scott; Ruprecht Wiedemeyer; Haiyan Yan; Carol Lim; Joseph Huang; Sabin Dhakal; Elena Ivanova; Yonghong Xiao; Hailei Zhang; Jian Hu; Jayne M Stommel; Michelle A Lee; An-Jou Chen; Ji-Hye Paik; Oreste Segatto; Cameron Brennan; Lisa A Elferink; Y Alan Wang; Lynda Chin; Ronald A DePinho
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-29       Impact factor: 11.205

3.  Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.

Authors:  Florent Tixier; Catherine Cheze Le Rest; Mathieu Hatt; Nidal Albarghach; Olivier Pradier; Jean-Philippe Metges; Laurent Corcos; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2011-02-14       Impact factor: 10.057

4.  Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results.

Authors:  Mu Zhou; Lawrence Hall; Dmitry Goldgof; Robin Russo; Yoganand Balagurunathan; Robert Gillies; Robert Gatenby
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

5.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.

Authors:  Francesca Ng; Balaji Ganeshan; Robert Kozarski; Kenneth A Miles; Vicky Goh
Journal:  Radiology       Date:  2012-11-14       Impact factor: 11.105

Review 6.  Tumor heterogeneity: causes and consequences.

Authors:  Andriy Marusyk; Kornelia Polyak
Journal:  Biochim Biophys Acta       Date:  2009-11-18

7.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients.

Authors:  Maciej Andrzej Mazurowski; Annick Desjardins; Jordan Milton Malof
Journal:  Neuro Oncol       Date:  2013-02-07       Impact factor: 12.300

Review 8.  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 9.  EGFR-targeted therapy in malignant glioma: novel aspects and mechanisms of drug resistance.

Authors:  Hui-Wen Lo
Journal:  Curr Mol Pharmacol       Date:  2010-01       Impact factor: 3.339

10.  EGFR wild-type amplification and activation promote invasion and development of glioblastoma independent of angiogenesis.

Authors:  Krishna M Talasila; Anke Soentgerath; Philipp Euskirchen; Gro V Rosland; Jian Wang; Peter C Huszthy; Lars Prestegarden; Kai Ove Skaftnesmo; Per Øystein Sakariassen; Eskil Eskilsson; Daniel Stieber; Olivier Keunen; Narve Brekka; Ingrid Moen; Janice M Nigro; Olav K Vintermyr; Morten Lund-Johansen; Simone Niclou; Sverre J Mørk; Per Oyvind Enger; Rolf Bjerkvig; Hrvoje Miletic
Journal:  Acta Neuropathol       Date:  2013-02-22       Impact factor: 17.088

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  13 in total

Review 1.  Towards precision medicine: from quantitative imaging to radiomics.

Authors:  U Rajendra Acharya; Yuki Hagiwara; Vidya K Sudarshan; Wai Yee Chan; Kwan Hoong Ng
Journal:  J Zhejiang Univ Sci B       Date:  2018 Jan.       Impact factor: 3.066

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.  Background, current role, and potential applications of radiogenomics.

Authors:  Katja Pinker; Fuki Shitano; Evis Sala; Richard K Do; Robert J Young; Andreas G Wibmer; Hedvig Hricak; Elizabeth J Sutton; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-11-02       Impact factor: 4.813

Review 4.  Radiomics in immuno-oncology.

Authors:  Z Bodalal; I Wamelink; S Trebeschi; R G H Beets-Tan
Journal:  Immunooncol Technol       Date:  2021-04-16

5.  Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction.

Authors:  Jiwoong Jeong; Liya Wang; Bing Ji; Yang Lei; Arif Ali; Tian Liu; Walter J Curran; Hui Mao; Xiaofeng Yang
Journal:  Quant Imaging Med Surg       Date:  2019-07

Review 6.  Imaging in neuro-oncology.

Authors:  Hari Nandu; Patrick Y Wen; Raymond Y Huang
Journal:  Ther Adv Neurol Disord       Date:  2018-02-28       Impact factor: 6.570

7.  Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone.

Authors:  Florent Tixier; Hyemin Um; Dalton Bermudez; Aditi Iyer; Aditya Apte; Maya S Graham; Kathryn S Nevel; Joseph O Deasy; Robert J Young; Harini Veeraraghavan
Journal:  Oncotarget       Date:  2019-01-18

8.  Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study.

Authors:  Prateek Prasanna; Jhimli Mitra; Niha Beig; Ameya Nayate; Jay Patel; Soumya Ghose; Rajat Thawani; Sasan Partovi; Anant Madabhushi; Pallavi Tiwari
Journal:  Sci Rep       Date:  2019-02-04       Impact factor: 4.379

Review 9.  Machine and deep learning methods for radiomics.

Authors:  Michele Avanzo; Lise Wei; Joseph Stancanello; Martin Vallières; Arvind Rao; Olivier Morin; Sarah A Mattonen; Issam El Naqa
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 10.  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

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