Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.
Glioma
Magnetic resonance imaging
Meta-analysis
O(6)-Methylguanine-DNA methyltransferase
Systematic review
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
03 Feb 2024
03 Feb 2024
Historique:
received:
23
09
2023
accepted:
31
12
2023
revised:
04
12
2023
medline:
3
2
2024
pubmed:
3
2
2024
entrez:
2
2
2024
Statut:
aheadofprint
Résumé
To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.
Identifiants
pubmed: 38308012
doi: 10.1007/s00330-024-10594-x
pii: 10.1007/s00330-024-10594-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Ostrom QT, Cioffi G, Gittleman H et al (2019) CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro Oncol 21:v1–v100. https://doi.org/10.1093/neuonc/noz150
doi: 10.1093/neuonc/noz150
pubmed: 31675094
pmcid: 6823730
Stupp R, Taillibert S, Kanner A et al (2017) Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma a randomized clinical trial. JAMA 318:2306–2316. https://doi.org/10.1001/jama.2017.18718
doi: 10.1001/jama.2017.18718
pubmed: 29260225
pmcid: 5820703
Stupp R, Mason WP, Van Den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996. https://doi.org/10.1056/NEJMoa043330
doi: 10.1056/NEJMoa043330
pubmed: 15758009
Weller M, Stupp R, Reifenberger G et al (2010) MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat Rev Neurol 6:39–51. https://doi.org/10.1038/nrneurol.2009.197
doi: 10.1038/nrneurol.2009.197
pubmed: 19997073
Esteller M, Hamilton SR, Burger PC et al (1999) Inactivation of the DNA repair gene O6-methylguanine-DNA methyltransferase by promoter hypermethylation is a common event in primary human neoplasia. Cancer Res 59:793–7
pubmed: 10029064
Johnson DR, O’Neill BP (2012) Glioblastoma survival in the United States before and during the temozolomide era. J Neurooncol 107:359–364. https://doi.org/10.1007/s11060-011-0749-4
doi: 10.1007/s11060-011-0749-4
pubmed: 22045118
Hegi ME, Diserens A-C, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003. https://doi.org/10.1056/nejmoa043331
doi: 10.1056/nejmoa043331
pubmed: 15758010
Poon MTC, Keni S, Vimalan V et al (2021) Extent of MGMT promoter methylation modifies the effect of temozolomide on overall survival in patients with glioblastoma: a regional cohort study. Neurooncol Adv 3:1–10. https://doi.org/10.1093/noajnl/vdab171
doi: 10.1093/noajnl/vdab171
Eoli M, Menghi F, Bruzzone MG et al (2007) Methylation of O6-methylguanine DNA methytransferase and loss of heterozygosity on 19q and/or 17p are overlapping features of secondary glioblastomas with prolonged survival. Clin Cancer Res 13:2606–2613. https://doi.org/10.1158/1078-0432.CCR-06-2184
doi: 10.1158/1078-0432.CCR-06-2184
pubmed: 17473190
Bruzzone MG, Eoli M, Cuccarini V et al (2009) Genetic signature of adult gliomas and correlation with MRI features. Expert Rev Mol Diagn 9:709–720. https://doi.org/10.1586/erm.09.44
doi: 10.1586/erm.09.44
pubmed: 19817555
Korfiatis P, Kline TL, Coufalova L et al (2016) MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys 43:2835–2844. https://doi.org/10.1118/1.4948668
doi: 10.1118/1.4948668
pubmed: 27277032
pmcid: 4866963
Li ZC, Bai H, Sun Q et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650. https://doi.org/10.1007/s00330-017-5302-1
doi: 10.1007/s00330-017-5302-1
pubmed: 29564594
Lotan E, Jain R, Razavian N et al (2019) State of the art: machine learning applications in glioma imaging. AJR Am J Roentgenol 212:26–37. https://doi.org/10.2214/AJR.18.20218
doi: 10.2214/AJR.18.20218
pubmed: 30332296
Le NQK, Do DT, Chiu FY et al (2020) XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma. J Pers Med 10:1–13. https://doi.org/10.3390/jpm10030128
doi: 10.3390/jpm10030128
Sohn B, An C, Kim D et al (2021) Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant. J Neurooncol 155:267–276. https://doi.org/10.1007/s11060-021-03870-z
doi: 10.1007/s11060-021-03870-z
pubmed: 34648115
pmcid: 8651601
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338. https://doi.org/10.1148/radiol.2020191145
doi: 10.1148/radiol.2020191145
pubmed: 32154773
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141
doi: 10.1038/nrclinonc.2017.141
pubmed: 28975929
Won SY, Park YW, Ahn SS et al (2021) Quality assessment of meningioma radiomics studies: bridging the gap between exploratory research and clinical applications. Eur J Radiol 138:109673. https://doi.org/10.1016/j.ejrad.2021.109673
doi: 10.1016/j.ejrad.2021.109673
pubmed: 33774441
Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30:523–536. https://doi.org/10.1007/S00330-019-06360-Z
doi: 10.1007/S00330-019-06360-Z
pubmed: 31350588
Sanduleanu S, Woodruff HC, de Jong EEC et al (2018) Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 127:349–360. https://doi.org/10.1016/J.RADONC.2018.03.033
doi: 10.1016/J.RADONC.2018.03.033
pubmed: 29779918
Spadarella G, Stanzione A, AkinciD’Antonoli T et al (2023) Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 33:1884–1894. https://doi.org/10.1007/s00330-022-09187-3
doi: 10.1007/s00330-022-09187-3
pubmed: 36282312
Collins GS, Reitsma JB, Altman DG, Moons KGM (2015) Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol 68:112–121. https://doi.org/10.1016/j.jclinepi.2014.11.010
doi: 10.1016/j.jclinepi.2014.11.010
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 131:803–820. https://doi.org/10.1007/s00401-016-1545-1
doi: 10.1007/s00401-016-1545-1
pubmed: 27157931
Whiting PF (2011) QUADAS-2: a revised tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med 155:529. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
doi: 10.7326/0003-4819-155-8-201110180-00009
pubmed: 22007046
Akinci D’Antonoli T, Cavallo AU, Vernuccio F et al (2023) Reproducibility of radiomics quality score: an intra- and inter-rater reliability study. Eur Radiol. https://doi.org/10.1007/s00330-023-10217-x
Park JE, Kim HS, Kim D et al (2020) A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 20:29. https://doi.org/10.1186/s12885-019-6504-5
doi: 10.1186/s12885-019-6504-5
pubmed: 31924170
pmcid: 6954557
Won SY, Park YW, Park M et al (2020) Quality reporting of radiomics analysis in mild cognitive impairment and Alzheimer’s disease: a roadmap for moving forward. Korean J Radiol 21:1345–1354. https://doi.org/10.3348/kjr.2020.0715
doi: 10.3348/kjr.2020.0715
pubmed: 33169553
pmcid: 7689149
Raudenbush SW (2009) Analyzing effect sizes: random effects models. The handbook of research synthesis and meta-analysis, 2nd edn. Russell Sage Foundation, New York, pp 295–315
Viechtbauer W (2005) Bias and efficiency of meta-analytic variance estimators in the random-effects model on JSTOR. In: J Educ Behav Stat. https://www.jstor.org/stable/3701379 . Accessed 13 Jul 2023
Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634. https://doi.org/10.1136/bmj.315.7109.629
doi: 10.1136/bmj.315.7109.629
pubmed: 9310563
pmcid: 2127453
Viechtbauer W (2010) Conducting meta-analyses in R with the metafor Package. J Stat Softw 36:1–48. https://doi.org/10.18637/jss.v036.i03
Calabrese E, Rudie JD, Rauschecker AM et al (2022) Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv 4:1–11. https://doi.org/10.1093/noajnl/vdac060
doi: 10.1093/noajnl/vdac060
Chen S, Xu Y, Ye M et al (2022) Predicting MGMT promoter methylation in diffuse gliomas using deep learning with radiomics. J Clin Med 11:3445. https://doi.org/10.3390/jcm11123445
doi: 10.3390/jcm11123445
pubmed: 35743511
pmcid: 9224690
Crisi G, Filice S (2020) Predicting MGMT promoter methylation of glioblastoma from dynamic susceptibility contrast perfusion: a radiomic approach. J Neuroimaging 30:458–462. https://doi.org/10.1111/jon.12724
doi: 10.1111/jon.12724
pubmed: 32374045
Do DT, Yang MR, Lam LHT et al (2022) Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. Sci Rep 12:1–12. https://doi.org/10.1038/s41598-022-17707-w
doi: 10.1038/s41598-022-17707-w
Hajianfar G, Shiri I, Maleki H et al (2019) Noninvasive O6 methylguanine-DNA methyltransferase status prediction in glioblastoma multiforme cancer using magnetic resonance imaging radiomics features: univariate and multivariate radiogenomics analysis. World Neurosurg 132:e140–e161. https://doi.org/10.1016/j.wneu.2019.08.232
doi: 10.1016/j.wneu.2019.08.232
pubmed: 31505292
Haubold J, Hosch R, Parmar V et al (2021) Fully automated MR based virtual biopsy of cerebral gliomas. Cancers 13:6186. https://doi.org/10.3390/cancers13246186
doi: 10.3390/cancers13246186
pubmed: 34944806
pmcid: 8699054
Haubold J, Demircioglu A, Gratz M et al (2020) Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting. Eur J Nucl Med Mol Imaging 47:1435–1445. https://doi.org/10.1007/s00259-019-04602-2
doi: 10.1007/s00259-019-04602-2
pubmed: 31811342
He J, Ren J, Niu G et al (2022) Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status. BMC Med Imaging 22:1–13. https://doi.org/10.1186/s12880-022-00865-8
doi: 10.1186/s12880-022-00865-8
Huang WY, Wen LH, Wu G et al (2021) Comparison of radiomics analyses based on different magnetic resonance imaging sequences in grading and molecular genomic typing of glioma. J Comput Assist Tomogr 45:110–120. https://doi.org/10.1097/RCT.0000000000001114
doi: 10.1097/RCT.0000000000001114
pubmed: 33475317
Huang W, Wen L, Wu G et al (2021) Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis. Cancer Sci 112:2835–2844. https://doi.org/10.1111/cas.14918
doi: 10.1111/cas.14918
pubmed: 33932065
pmcid: 8253278
Jiang C, Kong Z, Liu S et al (2019) Fusion radiomics features from conventional MRI predict MGMT promoter methylation status in lower grade gliomas. Eur J Radiol 121:108714. https://doi.org/10.1016/j.ejrad.2019.108714
doi: 10.1016/j.ejrad.2019.108714
pubmed: 31704598
Kihira S, Tsankova NM, Bauer A et al (2021) Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion. Neurooncol Adv 3:1–9. https://doi.org/10.1093/noajnl/vdab051
doi: 10.1093/noajnl/vdab051
Kihira S, Mei X, Mahmoudi K et al (2022) U-net based segmentation and characterization of gliomas. Cancers (Basel) 14:1–10. https://doi.org/10.3390/cancers14184457
doi: 10.3390/cancers14184457
Lu Y, Patel M, Natarajan K et al (2020) Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma. Magn Reson Imaging 74:161–170. https://doi.org/10.1016/j.mri.2020.09.017
doi: 10.1016/j.mri.2020.09.017
pubmed: 32980505
Pasquini L, Napolitano A, Lucignani M et al (2021) AI and high-grade glioma for diagnosis and outcome prediction: do all machine learning models perform equally well? Front Oncol 11:1–14. https://doi.org/10.3389/fonc.2021.601425
doi: 10.3389/fonc.2021.601425
Sasaki T, Kinoshita M, Fujita K et al (2019) Radiomics and MGMT promoter methylation for prognostication of newly diagnosed glioblastoma. Sci Rep 9:1–9. https://doi.org/10.1038/s41598-019-50849-y
doi: 10.1038/s41598-019-50849-y
Shboul ZA, Chen J, Iftekharuddin KM (2020) Prediction of molecular mutations in diffuse low-grade gliomas using MR imaging features. Sci Rep 10:1–13. https://doi.org/10.1038/s41598-020-60550-0
doi: 10.1038/s41598-020-60550-0
Verduin M, Primakov S, Compter I et al (2021) Prognostic and predictive value of integrated qualitative and quantitative magnetic resonance imaging analysis in glioblastoma. Cancers (Basel) 13:1–20. https://doi.org/10.3390/cancers13040722
doi: 10.3390/cancers13040722
Vils A, Bogowicz M, Tanadini-Lang S et al (2021) Radiomic analysis to predict outcome in recurrent glioblastoma based on multi-center MR imaging from the prospective DIRECTOR trial. Front Oncol 11:1–9. https://doi.org/10.3389/fonc.2021.636672
doi: 10.3389/fonc.2021.636672
Wei J, Yang G, Hao X et al (2019) A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 29:877–888. https://doi.org/10.1007/s00330-018-5575-z
doi: 10.1007/s00330-018-5575-z
pubmed: 30039219
Xi BY, Guo F, Xu ZL et al (2018) Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging 47:1380–1387. https://doi.org/10.1002/jmri.25860
doi: 10.1002/jmri.25860
pubmed: 28926163
Pease M, Gersey ZC, Ak M et al (2022) Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients. J Neurooncol 160:253–263. https://doi.org/10.1007/s11060-022-04150-0
doi: 10.1007/s11060-022-04150-0
pubmed: 36239836
Mansouri A, Hachem LD, Mansouri S et al (2019) MGMT promoter methylation status testing to guide therapy for glioblastoma: refining the approach based on emerging evidence and current challenges. Neuro Oncol 21:167–178. https://doi.org/10.1093/neuonc/noy132
doi: 10.1093/neuonc/noy132
pubmed: 30189035
Malmström A, Łysiak M, Kristensen BW et al (2020) Do we really know who has an MGMT methylated glioma? Results of an international survey regarding use of MGMT analyses for glioma. Neurooncol Pract 7:68–76. https://doi.org/10.1093/nop/npz039
doi: 10.1093/nop/npz039
pubmed: 32025325
Bakas S, Akbari H, Sotiras A et al (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:1–13. https://doi.org/10.1038/sdata.2017.117
doi: 10.1038/sdata.2017.117
Brancato V, Cerrone M, Lavitrano M et al (2022) A systematic review of the current status and quality of radiomics for glioma differential diagnosis. Cancers (Basel) 14:2731. https://doi.org/10.3390/cancers14112731
doi: 10.3390/cancers14112731
pubmed: 35681711
pmcid: 9179305
Vickers AJ, van Calster B, Steyerberg EW (2019) A simple, step-by-step guide to interpreting decision curve analysis. Diagnostic Progn Res 3:18. https://doi.org/10.1186/s41512-019-0064-7
doi: 10.1186/s41512-019-0064-7
Capogrosso P, Vickers AJ (2019) A systematic review of the literature demonstrates some errors in the use of decision curve analysis but generally correct interpretation of findings. Med Decis Mak 39:493–498. https://doi.org/10.1177/0272989X19832881
doi: 10.1177/0272989X19832881
Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20:1124–1137. https://doi.org/10.3348/kjr.2018.0070
doi: 10.3348/kjr.2018.0070
pubmed: 31270976
pmcid: 6609433
Kocak B, Baessler B, Bakas S et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:75. https://doi.org/10.1186/s13244-023-01415-8
doi: 10.1186/s13244-023-01415-8
pubmed: 37142815
pmcid: 10160267
Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14:e0224365. https://doi.org/10.1371/journal.pone.0224365
doi: 10.1371/journal.pone.0224365
pubmed: 31697686
pmcid: 6837442
Aoki K, Natsume A (2019) Overview of DNA methylation in adult diffuse gliomas. Brain Tumor Pathol 36:84–91. https://doi.org/10.1007/s10014-019-00339-w
doi: 10.1007/s10014-019-00339-w
pubmed: 30937703
Chen X, Zeng M, Tong Y et al (2020) Automatic prediction of MGMT status in glioblastoma via deep learning-based MR image analysis. Biomed Res Int 2020:9258649. https://doi.org/10.1155/2020/9258649
doi: 10.1155/2020/9258649
pubmed: 33029531
pmcid: 7530505
Saeed N, Ridzuan M, Alasmawi H et al (2023) MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. Med Image Anal 90:102989. https://doi.org/10.1016/j.media.2023.102989
doi: 10.1016/j.media.2023.102989
pubmed: 37827111