Literature DB >> 32315266

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient.

John Maynard1, Sachi Okuchi1, Stephen Wastling1, Ayisha Al Busaidi1, Ofran Almossawi1, Wonderboy Mbatha1, Sebastian Brandner1, Zane Jaunmuktane1, Ali Murat Koc1, Laura Mancini1, Rolf Jäger1, Stefanie Thust1.   

Abstract

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Entities:  

Year:  2020        PMID: 32315266     DOI: 10.1148/radiol.2020191832

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  16 in total

1.  Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

Authors:  Yoon Seong Choi; Sohi Bae; Jong Hee Chang; Seok-Gu Kang; Se Hoon Kim; Jinna Kim; Tyler Hyungtaek Rim; Seung Hong Choi; Rajan Jain; Seung-Koo Lee
Journal:  Neuro Oncol       Date:  2021-02-25       Impact factor: 12.300

2.  Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas.

Authors:  Arian Lasocki; Michael E Buckland; Katharine J Drummond; Heng Wei; Jing Xie; Michael Christie; Andrew Neal; Frank Gaillard
Journal:  Neuroradiology       Date:  2022-05-24       Impact factor: 2.804

Review 3.  2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist.

Authors:  Cillian McNamara; Kshitij Mankad; Stefanie Thust; Luke Dixon; Clara Limback-Stanic; Felice D'Arco; Thomas S Jacques; Ulrike Löbel
Journal:  Neuroradiology       Date:  2022-07-22       Impact factor: 2.995

4.  Integrated MRI-Immune-Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma.

Authors:  Giulia Mazzaschi; Alessandro Olivari; Antonio Pavarani; Costanza Anna Maria Lagrasta; Caterina Frati; Denise Madeddu; Bruno Lorusso; Silvia Dallasta; Chiara Tommasi; Antonino Musolino; Marcello Tiseo; Maria Michiara; Federico Quaini; Pellegrino Crafa
Journal:  Cancers (Basel)       Date:  2022-07-01       Impact factor: 6.575

Review 5.  The Use of 18F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds-A Narrative Review.

Authors:  Tineke van de Weijer; Martijn P G Broen; Rik P M Moonen; Ann Hoeben; Monique Anten; Koos Hovinga; Inge Compter; Jochem A J van der Pol; Cristina Mitea; Toine M Lodewick; Arnaud Jacquerie; Felix M Mottaghy; Joachim E Wildberger; Alida A Postma
Journal:  Diagnostics (Basel)       Date:  2022-05-11

6.  Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults.

Authors:  Stefanie Thust; Caroline Micallef; Sachi Okuchi; Sebastian Brandner; Atul Kumar; Kshitij Mankad; Stephen Wastling; Laura Mancini; Hans Rolf Jäger; Ananth Shankar
Journal:  Quant Imaging Med Surg       Date:  2021-01

7.  Regional and Volumetric Parameters for Diffusion-Weighted WHO Grade II and III Glioma Genotyping: A Method Comparison.

Authors:  S C Thust; J A Maynard; M Benenati; S J Wastling; L Mancini; Z Jaunmuktane; S Brandner; H R Jäger
Journal:  AJNR Am J Neuroradiol       Date:  2021-01-07       Impact factor: 3.825

Review 8.  Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors.

Authors:  Francesco Sanvito; Antonella Castellano; Andrea Falini
Journal:  Cancers (Basel)       Date:  2021-01-23       Impact factor: 6.639

9.  Worse prognosis for IDH wild-type diffuse gliomas with larger residual biological tumor burden.

Authors:  Hiroyuki Tatekawa; Hiroyuki Uetani; Akifumi Hagiwara; Shadfar Bahri; Catalina Raymond; Albert Lai; Timothy F Cloughesy; Phioanh L Nghiemphu; Linda M Liau; Whitney B Pope; Noriko Salamon; Benjamin M Ellingson
Journal:  Ann Nucl Med       Date:  2021-06-14       Impact factor: 2.668

10.  Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study.

Authors:  Jun Zhang; Hong Peng; Yu-Lin Wang; Hua-Feng Xiao; Yuan-Yuan Cui; Xiang-Bing Bian; De-Kang Zhang; Lin Ma
Journal:  Front Oncol       Date:  2021-05-13       Impact factor: 6.244

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