Top

Journal of Cancer Research and Clinical Oncology

Published in:

Open Access 01-03-2019 | Glioma | Original Article – Cancer Research

Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas

Authors: Shuang Wu, Jin Meng, Qi Yu, Ping Li, Shen Fu

Published in: Journal of Cancer Research and Clinical Oncology | Issue 3/2019

Abstract

Purpose

Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.

Methods

Eight classical machine learning methods were evaluated in terms of their stability and performance for pre-operative IDH genotype prediction. A total of 126 patients were enrolled for analysis. Overall, 704 radiomic features extracted from the pre-operative MRI images were analyzed. The patients were randomly assigned to either the training set or the validation set at a ratio of 2:1. Feature selection and classification model training were done using the training set, whereas the predictive performance and stability of the model were independently assessed using the validation set.

Results

Random Forest (RF) showed high predictive performance (accuracy 0.885 ± 0.041, AUC 0.931 ± 0.036), whereas neural network (NN) (accuracy 0.829 ± 0.064, AUC 0.878 ± 0.052) and flexible discriminant analysis (FDA) (accuracy 0.851 ± 0.049, AUC 0.875 ± 0.057) displayed low predictive performance. With regard to stability, RF also showed high robustness against data perturbation (relative standard deviations, RSD 3.87%).

Conclusions

RF is a promising machine learning method in predicting IDH genotype. Development of an accurate and reliable model can assist in the initial diagnostic evaluation and treatment planning for diffuse glioma patients.

Available only for authorised users

Aerts HJ et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006 CrossRefPubMed

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2017a) Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF.CrossRef

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2017b) Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q CrossRef

Bakas S et al (2017c) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:170117. https://doi.org/10.1038/sdata.2017.117 CrossRefPubMedPubMedCentral

Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

Branzoli F et al (2017) Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro Oncol. https://doi.org/10.1093/neuonc/nox214 CrossRefPubMedCentral

Cancer Genome Atlas Research N et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498. https://doi.org/10.1056/NEJMoa1402121 CrossRef

Carrillo JA et al (2012) Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR 33:1349–1355. https://doi.org/10.3174/ajnr.A2950 CrossRefPubMedPubMedCentral

Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069. https://doi.org/10.1016/j.crad.2004.07.008 CrossRefPubMed

Ceccarelli M et al (2016) Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164:550–563. https://doi.org/10.1016/j.cell.2015.12.028 CrossRefPubMedPubMedCentral

Chen P, Pan C (2018) Diabetes classification model based on boosting algorithms. BMC Bioinform 19:109. https://doi.org/10.1186/s12859-018-2090-9 CrossRef

Choi C et al (2016) Prospective longitudinal analysis of 2-Hydroxyglutarate magnetic resonance spectroscopy identifies broad clinical utility for the management of patients with IDH-mutant glioma. J Clin Oncol 34:4030–4039. https://doi.org/10.1200/jco.2016.67.1222 CrossRefPubMedPubMedCentral

Clark K et al (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057. https://doi.org/10.1007/s10278-013-9622-7 CrossRefPubMedPubMedCentral

Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRef

De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B (2013) mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics 29:2365–2368. https://doi.org/10.1093/bioinformatics/btt383 CrossRefPubMed

Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3:185–205CrossRefPubMed

Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genom 4:31. https://doi.org/10.1186/1755-8794-4-31 CrossRef

Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, New York

Fehr D et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA 112:E6265–E6273. https://doi.org/10.1073/pnas.1505935112 CrossRefPubMedPubMedCentral

Hartmann C et al (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120:707–718. https://doi.org/10.1007/s00401-010-0781-z CrossRefPubMed

Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270. https://doi.org/10.1080/01621459.1994.10476866 CrossRef

Huang Y et al (2016a) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957. https://doi.org/10.1148/radiol.2016152234 CrossRefPubMed

Huang YQ et al (2016b) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164. https://doi.org/10.1200/JCO.2015.65.9128 CrossRefPubMed

Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw. https://doi.org/10.18637/jss.v028.i05 CrossRef

Lambin P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446. https://doi.org/10.1016/j.ejca.2011.11.036 CrossRefPubMedPubMedCentral

Lambin P 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 CrossRefPubMed

Lee S 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 121:141–150. https://doi.org/10.1007/s11060-014-1614-z CrossRefPubMed

Li H et al (2016) Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. https://doi.org/10.1038/npjbcancer.2016.12 CrossRefPubMedPubMedCentral

Li Y et al (2018) MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol 28:356–362. https://doi.org/10.1007/s00330-017-4964-z CrossRefPubMed

Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2:18–22

Louis DN 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 CrossRefPubMed

Macyszyn L et al (2016) Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol 18:417–425. https://doi.org/10.1093/neuonc/nov127 CrossRefPubMed

Narang S et al (2017) Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma. Oncotarget 8:101244–101254. https://doi.org/10.18632/oncotarget.20643 CrossRefPubMedPubMedCentral

Nie K et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264. https://doi.org/10.1158/1078-0432.CCR-15-2997 CrossRefPubMed

O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257. https://doi.org/10.1158/1078-0432.CCR-14-0990 CrossRefPubMed

Ostrom QT, Gittleman H, Liao P, Vecchione-Koval T, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2017) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro Oncol 19:v1–v88. https://doi.org/10.1093/neuonc/nox158 CrossRefPubMedPubMedCentral

Park YW et al (2017) Prediction of IDH1-Mutation and 1p/19q-codeletion status using preoperative mr imaging phenotypes in lower grade gliomas. AJNR. https://doi.org/10.3174/ajnr.A5421 CrossRefPubMedPubMedCentral

Parmar C et al (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107. https://doi.org/10.1371/journal.pone.0102107 CrossRefPubMedPubMedCentral

Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015a) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272. https://doi.org/10.3389/fonc.2015.00272 CrossRefPubMedPubMedCentral

Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015b) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087. https://doi.org/10.1038/srep13087 CrossRefPubMedPubMedCentral

Parsons DW et al (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321:1807–1812. https://doi.org/10.1126/science.1164382 CrossRefPubMedPubMedCentral

Rathore S, Akbari H, Rozycki M, Bakas S, Davatzikos C (2016) NIMG-20. Imaging pattern analysis reveals three distinct phenotypic subtypes of gbm with different survival rates. Neuro Oncol 18:vi128–vi128. https://doi.org/10.1093/neuonc/now212.532 CrossRef

Rios Velazquez E et al (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77:3922–3930. https://doi.org/10.1158/0008-5472.CAN-17-0122 CrossRefPubMed

Rish I (2001) An Empirical Study of the Naïve Bayes Classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3. IBM, New York, pp 41–46

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12:77. https://doi.org/10.1186/1471-2105-12-77 CrossRefPubMedPubMedCentral

Xi YB et al (2017) Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25860 CrossRefPubMed

Yan H et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773. https://doi.org/10.1056/NEJMoa0808710 CrossRefPubMedPubMedCentral

Yu J et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509–3522. https://doi.org/10.1007/s00330-016-4653-3 CrossRefPubMed

Zhang B et al (2017a) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19:109–117. https://doi.org/10.1093/neuonc/now121 CrossRefPubMed

Zhang B et al (2017b) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27. https://doi.org/10.1016/j.canlet.2017.06.004 CrossRefPubMed

Zhang X et al (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magnetic Reson Imaging. https://doi.org/10.1002/jmri.25960 CrossRef

Title: Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas
Authors: Shuang Wu
Jin Meng
Qi Yu
Ping Li
Shen Fu
Publication date: 01-03-2019
Publisher: Springer Berlin Heidelberg
Keywords: Glioma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Glioma
Glioma
Published in: Journal of Cancer Research and Clinical Oncology / Issue 3/2019
Print ISSN: 0171-5216
Electronic ISSN: 1432-1335
DOI: https://doi.org/10.1007/s00432-018-2787-1

Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Reserve your place

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin

Prof. Florian Limbourg

Prof. Anoop Chauhan

Developed by: Springer Medicine

Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 3/2019

Melanoma tumour vasculature heterogeneity: from mice models to human

FTIR-based spectrum of salivary exosomes coupled with computational-aided discriminating analysis in the diagnosis of oral cancer

TP53 mutations as potential prognostic markers for specific cancers: analysis of data from The Cancer Genome Atlas and the International Agency for Research on Cancer TP53 Database

Increasing trends in in situ breast cancer incidence in a region with no population-based mammographic screening program: results from Zurich, Switzerland 2003–2014

Everolimus in patients with multiply relapsed or cisplatin refractory germ cell tumors: results of a phase II, single-arm, open-label multicenter trial (RADIT) of the German Testicular Cancer Study Group

Serum miR-16 as a potential biomarker for human cancer diagnosis: results from a large-scale population

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

At a glance: The STEP trials