Literature DB >> 33243896

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review.

A P Bhandari1,2, R Liong3, J Koppen2, S V Murthy4, A Lasocki5,6.   

Abstract

BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status.
PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve  = 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve  = 0.96, 90% sensitivity, 89% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines.
CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake.
© 2021 by American Journal of Neuroradiology.

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Year:  2020        PMID: 33243896      PMCID: PMC7814803          DOI: 10.3174/ajnr.A6875

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  49 in total

1.  Impact of the rise of artificial intelligence in radiology: What do radiologists think?

Authors:  Q Waymel; S Badr; X Demondion; A Cotten; T Jacques
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2.  Glioblastoma: clinical characteristics, prognostic factors and survival in 492 patients.

Authors:  Andreas M Stark; Julia van de Bergh; Jürgen Hedderich; H Maximilian Mehdorn; Arya Nabavi
Journal:  Clin Neurol Neurosurg       Date:  2012-02-27       Impact factor: 1.876

3.  MRI radiomics analysis of molecular alterations in low-grade gliomas.

Authors:  Ben Shofty; Moran Artzi; Dafna Ben Bashat; Gilad Liberman; Oz Haim; Alon Kashanian; Felix Bokstein; Deborah T Blumenthal; Zvi Ram; Tal Shahar
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-12-21       Impact factor: 2.924

4.  Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

Authors:  Burak Kocak; Emine Sebnem Durmaz; Ece Ates; Ipek Sel; Saime Turgut Gunes; Ozlem Korkmaz Kaya; Amalya Zeynalova; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2019-11-05       Impact factor: 5.315

5.  Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement.

Authors:  Matthew D F McInnes; David Moher; Brett D Thombs; Trevor A McGrath; Patrick M Bossuyt; Tammy Clifford; Jérémie F Cohen; Jonathan J Deeks; Constantine Gatsonis; Lotty Hooft; Harriet A Hunt; Christopher J Hyde; Daniël A Korevaar; Mariska M G Leeflang; Petra Macaskill; Johannes B Reitsma; Rachel Rodin; Anne W S Rutjes; Jean-Paul Salameh; Adrienne Stevens; Yemisi Takwoingi; Marcello Tonelli; Laura Weeks; Penny Whiting; Brian H Willis
Journal:  JAMA       Date:  2018-01-23       Impact factor: 56.272

6.  Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.

Authors:  Chia-Feng Lu; Fei-Ting Hsu; Kevin Li-Chun Hsieh; Yu-Chieh Jill Kao; Sho-Jen Cheng; Justin Bo-Kai Hsu; Ping-Huei Tsai; Ray-Jade Chen; Chao-Ching Huang; Yun Yen; Cheng-Yu Chen
Journal:  Clin Cancer Res       Date:  2018-05-22       Impact factor: 12.531

7.  A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

Authors:  Jasper van Hoek; Adrian Huber; Alexander Leichtle; Kirsi Härmä; Daniella Hilt; Hendrik von Tengg-Kobligk; Johannes Heverhagen; Alexander Poellinger
Journal:  Eur J Radiol       Date:  2019-11-09       Impact factor: 3.528

Review 8.  The practical implementation of artificial intelligence technologies in medicine.

Authors:  Jianxing He; Sally L Baxter; Jie Xu; Jiming Xu; Xingtao Zhou; Kang Zhang
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

9.  The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study.

Authors:  Martinus P G Broen; Marion Smits; Maarten M J Wijnenga; Hendrikus J Dubbink; Monique H M E Anten; Olaf E M G Schijns; Jan Beckervordersandforth; Alida A Postma; Martin J van den Bent
Journal:  Neuro Oncol       Date:  2018-09-03       Impact factor: 13.029

10.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

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  12 in total

1.  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

2.  Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.

Authors:  G I Cassinelli Petersen; J Shatalov; T Verma; W R Brim; H Subramanian; A Brackett; R C Bahar; S Merkaj; T Zeevi; L H Staib; J Cui; A Omuro; R A Bronen; A Malhotra; M S Aboian
Journal:  AJNR Am J Neuroradiol       Date:  2022-03-31       Impact factor: 3.825

3.  Quantitative relaxometry using synthetic MRI could be better than T2-FLAIR mismatch sign for differentiation of IDH-mutant gliomas: a pilot study.

Authors:  Kazufumi Kikuchi; Osamu Togao; Koji Yamashita; Daichi Momosaka; Yoshitomo Kikuchi; Daisuke Kuga; Nobuhiro Hata; Masahiro Mizoguchi; Hidetaka Yamamoto; Toru Iwaki; Akio Hiwatashi; Kousei Ishigami
Journal:  Sci Rep       Date:  2022-06-02       Impact factor: 4.996

Review 4.  Isocitrate Dehydrogenase Mutant Grade II and III Glial Neoplasms.

Authors:  Ingo K Mellinghoff; Susan M Chang; Kurt A Jaeckle; Martin van den Bent
Journal:  Hematol Oncol Clin North Am       Date:  2021-10-25       Impact factor: 2.861

5.  High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma.

Authors:  Fang Zhang; Hua-Bao Cai; Han-Ze Liu; Shen Gao; Bin Wang; Yang-Chun Hu; Hong-Wei Cheng; Jin-Xiu Liu; Yang Gao; Wen-Ming Hong
Journal:  Dis Markers       Date:  2022-04-21       Impact factor: 3.464

Review 6.  Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.

Authors:  Alberto Eugenio Tozzi; Francesco Fabozzi; Megan Eckley; Ileana Croci; Vito Andrea Dell'Anna; Erica Colantonio; Angela Mastronuzzi
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Review 7.  MRI biomarkers in neuro-oncology.

Authors:  Marion Smits
Journal:  Nat Rev Neurol       Date:  2021-06-20       Impact factor: 42.937

8.  Brain Tumor Imaging: Applications of Artificial Intelligence.

Authors:  Muhammad Afridi; Abhi Jain; Mariam Aboian; Seyedmehdi Payabvash
Journal:  Semin Ultrasound CT MR       Date:  2022-02-11       Impact factor: 1.875

Review 9.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

Authors:  Xingping Zhang; Yanchun Zhang; Guijuan Zhang; Xingting Qiu; Wenjun Tan; Xiaoxia Yin; Liefa Liao
Journal:  Front Oncol       Date:  2022-02-17       Impact factor: 6.244

Review 10.  Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis.

Authors:  Evi J van Kempen; Max Post; Manoj Mannil; Benno Kusters; Mark Ter Laan; Frederick J A Meijer; Dylan J H A Henssen
Journal:  Cancers (Basel)       Date:  2021-05-26       Impact factor: 6.639

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