nach oben

European Radiology

Erschienen in:

21.12.2016 | Magnetic Resonance

Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma

verfasst von: Jinhua Yu, Zhifeng Shi, Yuxi Lian, Zeju Li, Tongtong Liu, Yuan Gao, Yuanyuan Wang, Liang Chen, Ying Mao

Erschienen in: European Radiology | Ausgabe 8/2017

Einloggen, um Zugang zu erhalten

Abstract

Objective

The status of isocitrate dehydrogenase 1 (IDH1) is highly correlated with the development, treatment and prognosis of glioma. We explored a noninvasive method to reveal IDH1 status by using a quantitative radiomics approach for grade II glioma.

Methods

A primary cohort consisting of 110 patients pathologically diagnosed with grade II glioma was retrospectively studied. The radiomics method developed in this paper includes image segmentation, high-throughput feature extraction, radiomics sequencing, feature selection and classification. Using the leave-one-out cross-validation (LOOCV) method, the classification result was compared with the real IDH1 situation from Sanger sequencing. Another independent validation cohort containing 30 patients was utilised to further test the method.

Results

A total of 671 high-throughput features were extracted and quantized. 110 features were selected by improved genetic algorithm. In LOOCV, the noninvasive IDH1 status estimation based on the proposed approach presented an estimation accuracy of 0.80, sensitivity of 0.83 and specificity of 0.74. Area under the receiver operating characteristic curve reached 0.86. Further validation on the independent cohort of 30 patients produced similar results.

Conclusions

Radiomics is a potentially useful approach for estimating IDH1 mutation status noninvasively using conventional T2-FLAIR MRI images. The estimation accuracy could potentially be improved by using multiple imaging modalities.

Key Points

• Noninvasive IDH1 status estimation can be obtained with a radiomics approach.

• Automatic and quantitative processes were established for noninvasive biomarker estimation.

• High-throughput MRI features are highly correlated to IDH1 states.

• Area under the ROC curve of the proposed estimation method reached 0.86.

Nur mit Berechtigung zugänglich

Patrick YW, Santosh K (2008) Malignant Gliomas in Adults. N Engl J Med. doi:10.1056/NEJMra0708126

Wang Q, Zhang H, Zhang J et al (2016) The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis. Eur Radiol. doi:10.1007/s00330-015-4046-z

Venneti S, Huse JT (2015) The evolving molecular genetics of low-grade glioma. Adv Anat Pathol. doi:10.1097/PAP.0000000000000049 PubMedPubMedCentral

Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive. Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med. doi:10.1056/NEJMoa1402121 PubMed

Weller M, Pfister SM, Wick W, Hegi ME, Reifenberger G, Stupp R (2013) Molecular neuro-oncology in clinical practice: a new horizon. Lancet Oncol. doi:10.1016/S1470-2045(13)70168-2 PubMed

Beiko J, Suki D, Hess KR et al (2014) IDH1 mutant malignant astrocytomas are more amenable to surgical resection and have a survival benefit associated with maximal surgical resection. Neuro Oncol. doi:10.1093/neuonc/not159 PubMed

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. doi:10.1038/ncomms5006

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures. They Are Data. Radiology. doi:10.1148/radiol.2015151169

Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. doi:10.1088/0031-9155/60/14/5471 PubMed

10.

Wu W, Parmar C, Grossmann P et al (2016) Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Front Oncol. doi:10.3389/fonc.2016.00071

11.

Cameron A, Khalvati F, Haider M, Wong A (2015) MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection. IEEE Trans Biomed Eng. doi:10.1109/TBME.2015.2485779 PubMed

12.

Wang J, Kato F, Oyama-Manabe N et al (2015) Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PLoS One. doi:10.1371/journal.pone.0143308

13.

Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. Front Oncol. doi:10.3389/fonc.2015.00272

14.

Chan AK, Yao Y, Zhang Z et al (2015) TERT promoter mutations contribute to subset prognostication of lower-grade gliomas. Mod Pathol. doi:10.1038/modpathol.2014.94

15.

Pereira S, Pinto A, Alves V, Silva CA (2016) Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images. IEEE Trans Med Imaging. doi:10.1109/TMI.2016.2538465 PubMed

16.

Wechsler-Reya R, Scott MP (2001) The developmental biology of brain tumors. Annu Rev Neurosci. doi:10.1146/annurev.neuro.24.1.385 PubMed

17.

Mazziotta J, Toga A, Evans A et al (2001) A probabilistic atlas and reference system for the human brain. International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci. doi:10.1098/rstb.2001.0915 PubMedPubMedCentral

18.

Ellingson BM, Cloughesy TF, Pope WB et al (2012) Anatomic localization of O6-methylguanine DNA methyltransferase (MGMT) promoter methylated and unmethylated tumors: a radiographic study in 358 de novo human glioblastomas. Neuroimage. doi:10.1016/j.neuroimage.2011.09.076 PubMed

19.

Wang YY, Zhang T, Li SW et al (2015) Mapping p53 mutations in low-grade glioma: a voxel-based neuroimaging analysis. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A4065

20.

Wang Y, Zhang T, Li S et al (2015) Anatomical localization of isocitrate dehydrogenase 1 mutation: a voxel-based radiographic study of 146 low-grade gliomas. Eur J Neurol. doi:10.1111/ene.12578 PubMedCentral

21.

Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. doi:10.1006/nimg.2001.0978

22.

Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. doi:10.1016/j.mri.2003.09.001 PubMed

23.

Haralick RM, Shanmugam K, Dinstein I (1990) Textural features for image classification. IEEE Trans Syst Man Cybern B Cybern. doi:10.1109/TSMC.1973.4309314

24.

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TransEvolutComput. doi:10.1109/4235.996017

25.

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. doi:10.1109/TPAMI.2005.159 PubMed

26.

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn. doi:10.1007/BF00994018

27.

Nouretdinov I, Costafreda SG, Gammerman A et al (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage. doi:10.1016/j.neuroimage.2010.05.023 PubMed

28.

Schapire RE, Singer Y (1999) Improved Boosting Algorithms Using Confidence-rated Predictions. Mach Learn. doi:10.1145/279943.279960

29.

Maglietta R, Amoroso N, Boccardi M et al (2016) Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm. Pattern Anal Appl. doi:10.1007/s10044-015-0492-0 PubMed

30.

La Fuente MI, Young RJ, Rubel J et al (2016) Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro Oncol. doi:10.1093/neuonc/nov307 PubMed

31.

Lombardi G, Corona G, Bellu L et al (2015) Diagnostic value of plasma and urinary 2-hydroxyglutarate to identify patients with isocitrate dehydrogenase-mutated glioma. Oncologist. doi:10.1634/theoncologist.2014-0266 PubMedPubMedCentral

32.

Pope WB, Prins RM, Albert Thomas M et al (2012) Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neurooncol. doi:10.1007/s11060-011-0737-8 PubMedCentral

33.

Andronesi OC, Rapalino O, Gerstner E et al (2013) Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. J Clin Invest. doi:10.1172/JCI67229 PubMedPubMedCentral

34.

Togao O, Hiwatashi A, Yamashita K et al (2016) Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging. Eur Radiol. doi:10.1007/s00330-016-4328-0

35.

LLee S, Choi SH, Ryoo I et al (2015) Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol. doi:10.1007/s11060-014-1614-z

36.

Yamashita K, Hiwatashi A, Togao O et al (2016) MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A4491 PubMedCentral

Titel: Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma
verfasst von: Jinhua Yu
Zhifeng Shi
Yuxi Lian
Zeju Li
Tongtong Liu
Yuan Gao
Yuanyuan Wang
Liang Chen
Ying Mao
Publikationsdatum: 21.12.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2017
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-016-4653-3

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma