Literature DB >> 25956436

Differentiation of true-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventional MRI.

Xin Chen1, Xinhua Wei1, Zhongping Zhang2, Ruimeng Yang1, Yanjie Zhu3, Xinqing Jiang4.   

Abstract

Twenty-two patients with pathologically confirmed glioblastoma who had received concurrent CCRT with TMZ underwent conventional MRI including T1-weighted imaging(T1WI), T2-weighted imaging(T2WI), fluid attenuated inversion recovery(FLAIR)and contrast-enhanced T1WI(T1Ce). Five GLCM texture maps of contrast, energy, entropy, correlation and homogeneity were generated for each MRI series. Of the aforementioned 5 texture features, the most significant features were contrast and correlation on T2WI with areas under ROC curve of 0.883 and 0.892, respectively, and they had the same sensitivity of 75%, specificity of 100%, accuracy of 86.4%, PPV of 100% and NPV of 76.9% in differentiation true progression from pseudoprogression.
Copyright © 2015. Published by Elsevier Inc.

Entities:  

Keywords:  Glioblastoma; Gray level co-occurrence matric; Magnetic resonance imaging; Pseudoprogression; Texture analysis

Mesh:

Substances:

Year:  2015        PMID: 25956436     DOI: 10.1016/j.clinimag.2015.04.003

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  20 in total

1.  Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

Authors:  David Molina; Julián Pérez-Beteta; Belén Luque; Elena Arregui; Manuel Calvo; José M Borrás; Carlos López; Juan Martino; Carlos Velasquez; Beatriz Asenjo; Manuel Benavides; Ismael Herruzo; Alicia Martínez-González; Luis Pérez-Romasanta; Estanislao Arana; Víctor M Pérez-García
Journal:  Br J Radiol       Date:  2016-06-20       Impact factor: 3.039

2.  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

Review 3.  Application of 7T MRS to High-Grade Gliomas.

Authors:  L McCarthy; G Verma; G Hangel; A Neal; B A Moffat; J P Stockmann; O C Andronesi; P Balchandani; C G Hadjipanayis
Journal:  AJNR Am J Neuroradiol       Date:  2022-05-26       Impact factor: 4.966

4.  Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types.

Authors:  Jiahui Zhang; Xiaoli Wang; Lixia Zhang; Linpeng Yao; Xing Xue; Siying Zhang; Xin Li; Yuanjun Chen; Peipei Pang; Dongdong Sun; Juan Xu; Yanjun Shi; Feng Chen
Journal:  Ann Transl Med       Date:  2020-07

5.  Comparison between the Prebolus T1 Measurement and the Fixed T1 Value in Dynamic Contrast-Enhanced MR Imaging for the Differentiation of True Progression from Pseudoprogression in Glioblastoma Treated with Concurrent Radiation Therapy and Temozolomide Chemotherapy.

Authors:  J G Nam; K M Kang; S H Choi; W H Lim; R-E Yoo; J-H Kim; T J Yun; C-H Sohn
Journal:  AJNR Am J Neuroradiol       Date:  2017-10-26       Impact factor: 3.825

6.  MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.

Authors:  Ankush Bhatia; Maxwell Birger; Harini Veeraraghavan; Hyemin Um; Florent Tixier; Anna Sophia McKenney; Marina Cugliari; Annalise Caviasco; Angelica Bialczak; Rachna Malani; Jessica Flynn; Zhigang Zhang; T Jonathan Yang; Bianca D Santomasso; Alexander N Shoushtari; Robert J Young
Journal:  Neuro Oncol       Date:  2019-12-17       Impact factor: 12.300

7.  Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation.

Authors:  Xiaohua Qian; Hua Tan; Jian Zhang; Weilin Zhao; Michael D Chan; Xiaobo Zhou
Journal:  Med Phys       Date:  2016-11       Impact factor: 4.071

8.  Pseudoprogression as an adverse event of glioblastoma therapy.

Authors:  Carmen Balaña; Jaume Capellades; Estela Pineda; Anna Estival; Josep Puig; Sira Domenech; Eugenia Verger; Teresa Pujol; Maria Martinez-García; Laura Oleaga; JoseMaria Velarde; Carlos Mesia; Rafael Fuentes; Jordi Marruecos; Sonia Del Barco; Salvador Villà; Cristina Carrato; Oscar Gallego; Miguel Gil-Gil; Jordi Craven-Bartle; Francesc Alameda
Journal:  Cancer Med       Date:  2017-11-03       Impact factor: 4.452

9.  Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma.

Authors:  Bum-Sup Jang; Seung Hyuck Jeon; Il Han Kim; In Ah Kim
Journal:  Sci Rep       Date:  2018-08-21       Impact factor: 4.379

10.  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

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