Literature DB >> 23224678

Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade.

Karoline Skogen1, Balaji Ganeshan, Catriona Good, Giles Critchley, Ken Miles.   

Abstract

To undertake a preliminary study that uses CT texture analysis (CTTA) to quantify heterogeneity in gliomas on contrast-enhanced CT and to assess the relationship between tumour heterogeneity and grade. Retrospective analysis of contrast enhanced CT images was performed in 44 patients with histologically proven cerebral glioma between 2007 and 2010. 11 tumours were low grade (Grade I = 3; Grade II, = 8) and 33 high grade (Grade III = 10, Grade IV = 23). CTTA assessment of tumour heterogeneity was performed using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity was quantified as standard deviation (SD) with or without filtration. Tumour heterogeneity, size and attenuation were correlated with tumour grade. For each parameter, receiver operating characteristics characterised the diagnostic performance for discrimination of high grade from low grade glioma and of grade III tumours from grade IV. Further the CTTA was compared to the radiological diagnosis. Tumour heterogeneity correlated significantly with grade (SD without filtration rs = 0.664, p < 0.001, SD with coarse filtration (rs = 0.714, p < 0.001). Tumour size and attenuation showed only moderate correlations with tumour grade (rs = 0.426, p = 0.004 and rs = 0.447, p = 0.002 respectively). Coarse texture was the best discriminator between high and low grade tumours (AUC 0.832, p < 0.0001) and between grade III and grade IV gliomas (AUC = 0.878 p = 0.0001). Compared to the radiological diagnosis, CTTA further characterised the indetermined cases. By quantifying tumour heterogeneity, CTTA has the potential to provide a marker of tumour grade for patients with cerebral glioma. By differentiating between high and low grade tumours, CTTA could possibly assist clinical management.

Entities:  

Mesh:

Year:  2012        PMID: 23224678     DOI: 10.1007/s11060-012-1010-5

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  30 in total

1.  High-grade gliomas.

Authors:  Brett J Theeler; Morris D Groves
Journal:  Curr Treat Options Neurol       Date:  2011-08       Impact factor: 3.598

2.  Role of computed tomography in the management of adult brain tumours.

Authors:  R A Arogundade; G O G Awosanya; S O Arigbabu
Journal:  Niger Postgrad Med J       Date:  2006-06

3.  In search of biologic correlates for liver texture on portal-phase CT.

Authors:  Balaji Ganeshan; Kenneth A Miles; Rupert C D Young; Chris R Chatwin
Journal:  Acad Radiol       Date:  2007-09       Impact factor: 3.173

4.  Heterogeneity of renal cortical circulation in hypertension assessed by dynamic computed tomography.

Authors:  S Kojima; Y Yoshitomi; M Yano; M Saotome; K Tanaka; M Endo; M Kuramochi
Journal:  Am J Hypertens       Date:  2000-04       Impact factor: 2.689

5.  Angiogenesis and blood-brain barrier breakdown modulate CT contrast enhancement: an experimental study in a rabbit brain-tumor model.

Authors:  D Zagzag; M Goldenberg; S Brem
Journal:  AJR Am J Roentgenol       Date:  1989-07       Impact factor: 3.959

6.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.

Authors:  Balaji Ganeshan; Elleny Panayiotou; Kate Burnand; Sabina Dizdarevic; Ken Miles
Journal:  Eur Radiol       Date:  2011-11-17       Impact factor: 5.315

7.  Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas.

Authors:  Andrés Server; Bettina Kulle; Øystein B Gadmar; Roger Josefsen; Theresa Kumar; Per H Nakstad
Journal:  Eur J Radiol       Date:  2010-08-13       Impact factor: 3.528

8.  Three-dimensional selective-scale texture analysis of computed tomography pulmonary angiograms.

Authors:  Balaji Ganeshan; Kenneth A Miles; Rupert C D Young; Chris R Chatwin
Journal:  Invest Radiol       Date:  2008-06       Impact factor: 6.016

9.  Unreliability of contemporary neurodiagnostic imaging in evaluating suspected adult supratentorial (low-grade) astrocytoma.

Authors:  D Kondziolka; L D Lunsford; A J Martinez
Journal:  J Neurosurg       Date:  1993-10       Impact factor: 5.115

10.  MR tissue characterization of intracranial tumors by means of texture analysis.

Authors:  L R Schad; S Blüml; I Zuna
Journal:  Magn Reson Imaging       Date:  1993       Impact factor: 2.546

View more
  29 in total

1.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

Authors:  Thibaud P Coroller; Patrick Grossmann; Ying Hou; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Gretchen Hermann; Philippe Lambin; Benjamin Haibe-Kains; Raymond H Mak; Hugo J W L Aerts
Journal:  Radiother Oncol       Date:  2015-03-04       Impact factor: 6.280

Review 2.  Deep learning with convolutional neural network in radiology.

Authors:  Koichiro Yasaka; Hiroyuki Akai; Akira Kunimatsu; Shigeru Kiryu; Osamu Abe
Journal:  Jpn J Radiol       Date:  2018-03-01       Impact factor: 2.374

3.  MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

Authors:  Jian Guo; Zhenyu Liu; Chen Shen; Zheng Li; Fei Yan; Jie Tian; Junfang Xian
Journal:  Eur Radiol       Date:  2018-04-09       Impact factor: 5.315

4.  Discrimination of HPV status using CT texture analysis: tumour heterogeneity in oropharyngeal squamous cell carcinomas.

Authors:  Ji Young Lee; Miran Han; Kap Seon Kim; Su-Jin Shin; Jin Wook Choi; Eun Ju Ha
Journal:  Neuroradiology       Date:  2019-10-22       Impact factor: 2.804

5.  CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.

Authors:  Siva P Raman; Yifei Chen; James L Schroeder; Peng Huang; Elliot K Fishman
Journal:  Acad Radiol       Date:  2014-09-16       Impact factor: 3.173

6.  Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT.

Authors:  Choong Guen Chee; Min A Yoon; Kyung Won Kim; Yusun Ko; Su Jung Ham; Young Chul Cho; Bumwoo Park; Hye Won Chung
Journal:  Eur Radiol       Date:  2021-03-19       Impact factor: 5.315

7.  Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest.

Authors:  Shan Wang; Meng Meng; Xue Zhang; Chen Wu; Ru Wang; Jiangfen Wu; Muhammad Umair Sami; Kai Xu
Journal:  Oncol Lett       Date:  2018-03-12       Impact factor: 2.967

8.  Measuring Computed Tomography Scanner Variability of Radiomics Features.

Authors:  Dennis Mackin; Xenia Fave; Lifei Zhang; David Fried; Jinzhong Yang; Brian Taylor; Edgardo Rodriguez-Rivera; Cristina Dodge; Aaron Kyle Jones; Laurence Court
Journal:  Invest Radiol       Date:  2015-11       Impact factor: 6.016

9.  Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix.

Authors:  Maria Ciolina; Valeria Vinci; Laura Villani; Silvia Gigli; Matteo Saldari; Pierluigi Benedetti Panici; Giorgia Perniola; Andrea Laghi; Carlo Catalano; Lucia Manganaro
Journal:  Radiol Med       Date:  2019-06-28       Impact factor: 3.469

10.  Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort.

Authors:  Linda M Pak; Jayasree Chakraborty; Mithat Gonen; William C Chapman; Richard K G Do; Bas Groot Koerkamp; Kees Verhoef; Ser Yee Lee; Marco Massani; Eric P van der Stok; Amber L Simpson
Journal:  J Am Coll Surg       Date:  2018-02-15       Impact factor: 6.113

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.