Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)

Author:

Stadlbauer Andreas123ORCID,Nikolic Katarina14,Oberndorfer Stefan14ORCID,Marhold Franz15ORCID,Kinfe Thomas M.36ORCID,Meyer-Bäse Anke7,Bistrian Diana Alina8ORCID,Schnell Oliver3,Doerfler Arnd9

Affiliation:

1. Karl Landsteiner University of Health Sciences, 3500 Krems, Austria

2. Institute of Medical Radiology, Diagnostics, Intervention, University Hospital St. Pölten, 3100 St. Pölten, Austria

3. Department of Neurosurgery, Universitätsklinikum Erlangen, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany

4. Division of Neurology, University Hospital St. Pölten, 3100 St. Pölten, Austria

5. Division of Neurosurgery, University Hospital St. Pölten, 3100 St. Pölten, Austria

6. Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany

7. Department of Scientific Computing, Florida State University, 400 Dirac Science Library Tallahassee, Tallahassee, FL 32306-4120, USA

8. Department of Electrical Engineering and Industrial Informatics, Politehnica University of Timisoara, 300006 Timișoara, Romania

9. Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany

Abstract

The mutational status of the isocitrate dehydrogenase (IDH) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 participants for independent external testing) were enrolled using two different physio-metabolic MRI protocols. The algorithms trained with physio-metabolic data demonstrated the best classification performance in independent internal testing: precision, 91.7%; accuracy, 87.5%; area under the receiver operating curve (AUROC), 0.979. In external testing, traditional machine learning models trained with cMRI data exhibited the best IDH classification results: precision, 84.9%; accuracy, 81.8%; and AUROC, 0.879. The poor performance for the physio-metabolic MRI approach appears to be explainable by site-dependent differences in data acquisition methodologies. The physio-metabolic MRI approach potentially supports reliable classification of IDH gene status in the presurgical stage of glioma patients. However, non-standardized protocols limit the level of evidence and underlie the need for a reproducible framework of data acquisition techniques.

Funder

German Research Foundation

Forschungsimpulse

Publisher

MDPI AG

Reference96 articles.

1. Genetic and molecular epidemiology of adult diffuse glioma;Molinaro;Nat. Rev. Neurol.,2019

2. Beyond the World Health Organization grading of infiltrating gliomas: Advances in the molecular genetics of glioma classification;Vigneswaran;Ann. Transl. Med.,2015

3. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary;Louis;Neuro Oncol.,2021

4. An integrated genomic analysis of human glioblastoma multiforme;Parsons;Science,2008

5. IDH1 and IDH2 mutations in gliomas;Yan;N. Engl. J. Med.,2009

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3