Literature DB >> 26368923

Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme.

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

Abstract

One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (≤12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in Glioblastoma multiforme.

Entities:  

Mesh:

Year:  2015        PMID: 26368923      PMCID: PMC4569439          DOI: 10.1371/journal.pone.0136557

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  18 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.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data.

Authors:  J G Sled; A P Zijdenbos; A C Evans
Journal:  IEEE Trans Med Imaging       Date:  1998-02       Impact factor: 10.048

3.  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

4.  Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma.

Authors:  Nai-Ming Cheng; Yu-Hua Dean Fang; Joseph Tung-Chieh Chang; Chung-Guei Huang; Din-Li Tsan; Shu-Hang Ng; Hung-Ming Wang; Chien-Yu Lin; Chun-Ta Liao; Tzu-Chen Yen
Journal:  J Nucl Med       Date:  2013-09-16       Impact factor: 10.057

5.  Overall survival of newly diagnosed glioblastoma patients receiving carmustine wafers followed by radiation and concurrent temozolomide plus rotational multiagent chemotherapy.

Authors:  Mary Lou Affronti; Christopher R Heery; James E Herndon; Jeremy N Rich; David A Reardon; Annick Desjardins; James J Vredenburgh; Allan H Friedman; Darell D Bigner; Henry S Friedman
Journal:  Cancer       Date:  2009-08-01       Impact factor: 6.860

6.  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 7.  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

8.  Spatial organization and correlations of cell nuclei in brain tumors.

Authors:  Yang Jiao; Hal Berman; Tim-Rasmus Kiehl; Salvatore Torquato
Journal:  PLoS One       Date:  2011-11-16       Impact factor: 3.240

9.  Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.

Authors:  Olya Grove; Anders E Berglund; Matthew B Schabath; Hugo J W L Aerts; Andre Dekker; Hua Wang; Emmanuel Rios Velazquez; Philippe Lambin; Yuhua Gu; Yoganand Balagurunathan; Edward Eikman; Robert A Gatenby; Steven Eschrich; Robert J Gillies
Journal:  PLoS One       Date:  2015-03-04       Impact factor: 3.240

10.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

View more
  14 in total

1.  MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma.

Authors:  Lina Zhao; Jie Gong; Yibin Xi; Man Xu; Chen Li; Xiaowei Kang; Yutian Yin; Wei Qin; Hong Yin; Mei Shi
Journal:  Eur Radiol       Date:  2019-08-01       Impact factor: 5.315

Review 2.  Advanced MRI Techniques in the Monitoring of Treatment of Gliomas.

Authors:  Harpreet Hyare; Steffi Thust; Jeremy Rees
Journal:  Curr Treat Options Neurol       Date:  2017-03       Impact factor: 3.598

3.  Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients.

Authors:  Ahmad Chaddad; Camel Tanougast
Journal:  Med Biol Eng Comput       Date:  2016-03-10       Impact factor: 2.602

4.  A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.

Authors:  Ahmad Chaddad; Christian Desrosiers; Lama Hassan; Camel Tanougast
Journal:  Br J Radiol       Date:  2016-10-26       Impact factor: 3.039

5.  A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours.

Authors:  J Pérez-Beteta; A Martínez-González; V M Pérez-García
Journal:  J R Soc Interface       Date:  2018-12-21       Impact factor: 4.118

6.  Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294.

Authors:  Kiaran P McGee; Ken-Pin Hwang; Daniel C Sullivan; John Kurhanewicz; Yanle Hu; Jihong Wang; Wen Li; Josef Debbins; Eric Paulson; Jeffrey R Olsen; Chia-Ho Hua; Lizette Warner; Daniel Ma; Eduardo Moros; Neelam Tyagi; Caroline Chung
Journal:  Med Phys       Date:  2021-05-20       Impact factor: 4.071

7.  Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer.

Authors:  Anum K Syed; Jennifer G Whisenant; Stephanie L Barnes; Anna G Sorace; Thomas E Yankeelov
Journal:  Cancers (Basel)       Date:  2020-06-24       Impact factor: 6.639

8.  Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers.

Authors:  Nestor Andres Parra; Hong Lu; Jung Choi; Kenneth Gage; Julio Pow-Sang; Robert J Gillies; Yoganand Balagurunathan
Journal:  Tomography       Date:  2019-03

9.  Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma.

Authors:  Katherine Dextraze; Abhijoy Saha; Donnie Kim; Shivali Narang; Michael Lehrer; Anita Rao; Saphal Narang; Dinesh Rao; Salmaan Ahmed; Venkatesh Madhugiri; Clifton David Fuller; Michelle M Kim; Sunil Krishnan; Ganesh Rao; Arvind Rao
Journal:  Oncotarget       Date:  2017-12-05

Review 10.  Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response.

Authors:  Maikel Verduin; Inge Compter; Danny Steijvers; Alida A Postma; Daniëlle B P Eekers; Monique M Anten; Linda Ackermans; Mark Ter Laan; Ralph T H Leijenaar; Tineke van de Weijer; Vivianne C G Tjan-Heijnen; Ann Hoeben; Marc Vooijs
Journal:  Dis Markers       Date:  2018-01-17       Impact factor: 3.434

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.