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European Radiology
Published in: European Radiology 2/2023

19-08-2022 | Glioma | Imaging Informatics and Artificial Intelligence

Multicenter clinical radiomics–integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas

Authors: Liqiang Zhang, Hongyu Pan, Zhi Liu, Jueni Gao, Xinyi Xu, Linlin Wang, Jie Wang, Yi Tang, Xu Cao, Yubo Kan, Zhipeng Wen, Jianjun Chen, Dingde Huang, Shanxiong Chen, Yongmei Li

Published in: European Radiology | Issue 2/2023

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Abstract

Objectives

To develop a clinical radiomics–integrated model based on 18 F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and multi-modal MRI for predicting alpha thalassemia/mental retardation X-linked (ATRX) mutation status of IDH-mutant lower-grade gliomas (LGGs).

Methods

One hundred and two patients (47 ATRX mutant-type, 55 ATRX wild-type) diagnosed with IDH-mutant LGGs (CNS WHO grades 1 and 2) were retrospectively enrolled. A total of 5540 radiomics features were extracted from structural MR (sMR) images (contrast-enhanced T1-weighted imaging, CE-T1WI; T2-weighted imaging, and T2WI), functional MR (fMR) images (apparent diffusion coefficient, ADC; cerebral blood volume, CBV), and metabolic PET images ([18F]FDG PET). The random forest algorithm was used to establish a clinical radiomics–integrated model, integrating the optimal multi-modal radiomics model with three clinical parameters. The predictive effectiveness of the models was evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results

The optimal multi-modal model incorporated sMR (CE-T1WI), fMR (ADC), and metabolic ([18F]FDG) images ([18F]FDG PET+ADC+ CE-T1WI) with the area under curves (AUCs) in the training and test groups of 0.971 and 0.962, respectively. The clinical radiomics–integrated model, incorporating [18F]FDG PET+ADC+CE-T1WI, three clinical parameters (KPS, SFSD, and ATGR), showed the best predictive effectiveness in the training and test groups (0.987 and 0.975, respectively).

Conclusions

The clinical radiomics–integrated model with metabolic, structural, and functional information based on [18F]FDG PET and multi-modal MRI achieved promising performance for predicting the ATRX mutation status of IDH-mutant LGGs.

Key Points

• The clinical radiomics–integrated model based on [18F]FDG PET and multi-modal MRI achieved promising performance for predicting ATRX mutation status in LGGs.
• The study investigated the value of multicenter clinical radiomics–integrated model based on [18F]FDG PET and multi-modal MRI in LGGs regarding ATRX mutation status prediction.
• The integrated model provided structural, functional, and metabolic information simultaneously and demonstrated with satisfactory calibration and discrimination in the training and test groups (0.987 and 0.975, respectively).
Appendix
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Literature
1.
Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820CrossRef
2.
Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498CrossRef
3.
Pekmezci M, Rice T, Molinaro AM et al (2017) Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT. Acta Neuropathol 133:1001–1016CrossRef
4.
Li L, Mu W, Wang Y et al (2019) A non-invasive radiomic method using (18)F-FDG PET predicts isocitrate dehydrogenase genotype and prognosis in patients with glioma. Front Oncol 9:1183CrossRef
5.
Kong Z, Lin Y, Jiang C et al (2019) (18)F-FDG-PET-based Radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma. Cancer Imaging 19:58CrossRef
6.
Arita H, Kinoshita M, Kawaguchi A et al (2018) Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep 8:11773CrossRef
7.
Kocak B, Durmaz ES, Ates E et al (2020) Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol 30:877–886CrossRef
8.
Reuss DE, Sahm F, Schrimpf D et al (2015) ATRX and IDH1-R132H immunohistochemistry with subsequent copy number analysis and IDH sequencing as a basis for an “integrated” diagnostic approach for adult astrocytoma, oligodendroglioma and glioblastoma. Acta Neuropathol 129:133–146CrossRef
9.
Kinoshita M, Sakai M, Arita H et al (2016) Introduction of high throughput magnetic resonance T2-weighted image texture analysis for WHO grade 2 and 3 gliomas. PLoS One 11:e0164268CrossRef
10.
Ricard D, Kaloshi G, Amiel-Benouaich A et al (2007) Dynamic history of low-grade gliomas before and after temozolomide treatment. Ann Neurol 61:484–490CrossRef
11.
Wang Y, Fan X, Li H et al (2015) Tumor border sharpness correlates with HLA-G expression in low-grade gliomas. J Neuroimmunol 282:1–6CrossRef
12.
Pauleit D, Floeth F, Hamacher K et al (2005) O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain J Neurol 128:678–687CrossRef
13.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044CrossRef
14.
Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Crainiceanu CM (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19CrossRef
15.
Lee EJ, terBrugge K, Mikulis D et al (2011) Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol 196:71–76CrossRef
16.
Egger J, Kapur T, Fedorov A et al (2013) GBM volumetry using the 3D slicer medical image computing platform. Sci Rep 3:1364CrossRef
17.
Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26:5512–5528CrossRef
18.
Archer DB, Bricker JT, Chu WT et al (2019) Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study. Lancet Digit Health 1:e222–e231CrossRef
19.
Wu TT, Chen YF, Hastie T, Sobel E, Lange K (2009) Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25:714–721CrossRef
20.
Gui J, Li H (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21:3001–3008CrossRef
21.
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238CrossRef
22.
Modrek AS, Golub D, Khan T et al (2017) Low-grade astrocytoma mutations in IDH1, P53, and ATRX cooperate to block differentiation of human neural stem cells via repression of SOX2. Cell Rep 21:1267–1280CrossRef
23.
Xie Y, Tan Y, Yang C et al (2019) Omics-based integrated analysis identified ATRX as a biomarker associated with glioma diagnosis and prognosis. Cancer Biol Med 16:784–796CrossRef
24.
Wang Y, Yang J, Wild AT et al (2019) G-quadruplex DNA drives genomic instability and represents a targetable molecular abnormality in ATRX-deficient malignant glioma. Nat Commun 10:943CrossRef
25.
Han B, Cai J, Gao W et al (2018) Loss of ATRX suppresses ATM dependent DNA damage repair by modulating H3K9me3 to enhance temozolomide sensitivity in glioma. Cancer Lett 419:280–290CrossRef
26.
Li Y, Liu X, Qian Z et al (2018) Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature. Eur Radiol 28:2960–2968CrossRef
27.
Ren Y, Zhang X, Rui W et al (2019) Noninvasive prediction of IDH1 mutation and ATRX expression loss in low-grade gliomas using multiparametric MR radiomic features. J Magn Reson Imaging 49:808–817CrossRef
28.
Galldiks N, Stoffels G, Filss C et al (2015) The use of dynamic O-(2-18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro Oncol 17:1293–1300
29.
Maher EA, Marin-Valencia I, Bachoo RM et al (2012) Metabolism of [U-13 C]glucose in human brain tumors in vivo. NMR Biomed 25:1234–1244CrossRef
30.
Ogishima T, Tamura K, Kobayashi D et al (2017) ATRX status correlates with 11 C-methionine uptake in WHO grade II and III gliomas with IDH1 mutations. Brain Tumor Pathol 34:20–27CrossRef
31.
Law I, Albert NL, Arbizu J et al (2019) Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [(18)F]FDG: version 1.0. Eur J Nucl Med Mol Imaging 46:540–557CrossRef
Metadata
Title
Multicenter clinical radiomics–integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas
Authors
Liqiang Zhang
Hongyu Pan
Zhi Liu
Jueni Gao
Xinyi Xu
Linlin Wang
Jie Wang
Yi Tang
Xu Cao
Yubo Kan
Zhipeng Wen
Jianjun Chen
Dingde Huang
Shanxiong Chen
Yongmei Li
Publication date
19-08-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 2/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-09043-4

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