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European Radiology
Published in: European Radiology 2/2022

01-02-2022 | Glioma | Imaging Informatics and Artificial Intelligence

Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning

Authors: Yiming Li, Dong Wei, Xing Liu, Xing Fan, Kai Wang, Shaowu Li, Zhong Zhang, Kai Ma, Tianyi Qian, Tao Jiang, Yefeng Zheng, Yinyan Wang

Published in: European Radiology | Issue 2/2022

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Abstract

Objectives

The molecular subtyping of diffuse gliomas is important. The aim of this study was to establish predictive models based on preoperative multiparametric MRI.

Methods

A total of 1016 diffuse glioma patients were retrospectively collected from Beijing Tiantan Hospital. Patients were randomly divided into the training (n = 780) and validation (n = 236) sets. According to the 2016 WHO classification, diffuse gliomas can be classified into four binary classification tasks (tasks I–IV). Predictive models based on radiomics and deep convolutional neural network (DCNN) were developed respectively, and their performances were compared with receiver operating characteristic (ROC) curves. Additionally, the radiomics and DCNN features were visualized and compared with the t-distributed stochastic neighbor embedding technique and Spearman’s correlation test.

Results

In the training set, areas under the curves (AUCs) of the DCNN models (ranging from 0.99 to 1.00) outperformed the radiomics models in all tasks, and the accuracies of the DCNN models (ranging from 0.90 to 0.94) outperformed the radiomics models in tasks I, II, and III. In the independent validation set, the accuracies of the DCNN models outperformed the radiomics models in all tasks (0.74–0.83), and the AUCs of the DCNN models (0.85–0.89) outperformed the radiomics models in tasks I, II, and III. DCNN features demonstrated more superior discriminative capability than the radiomics features in feature visualization analysis, and their general correlations were weak.

Conclusions

Both the radiomics and DCNN models could preoperatively predict the molecular subtypes of diffuse gliomas, and the latter performed better in most circumstances.

Key Points

• The molecular subtypes of diffuse gliomas could be predicted with MRI.
• Deep learning features tend to outperform radiomics features in large cohorts.
• The correlation between the radiomics features and DCNN features was low.
Appendix
Available only for authorised users
Literature
1.
Molinaro AM, Taylor JW, Wiencke JK, Wrensch MR (2019) Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol 15:405–417CrossRef
2.
Jiang T, Mao Y, Ma W et al (2016) CGCG clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett 375:263–273CrossRef
3.
Louis DN, Ohgaki H, Wiestler OD et al (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109CrossRef
4.
van den Bent MJ (2010) Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol 120:297–304CrossRef
5.
Sturm D, Orr BA, Toprak UH et al (2016) New brain tumor entities emerge from molecular classification of CNS-PNETs. Cell 164:1060–1072CrossRef
6.
Lin AL, DeAngelis LM (2017) Reappraising the 2016 WHO classification for diffuse glioma. Neuro Oncol 19:609–610CrossRef
7.
Lapointe S, Perry A, Butowski NA (2018) Primary brain tumours in adults. The Lancet 392:432–446CrossRef
8.
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
9.
Reifenberger G, Wirsching HG, Knobbe-Thomsen CB, Weller M (2017) Advances in the molecular genetics of gliomas - implications for classification and therapy. Nat Rev Clin Oncol 14:434–452CrossRef
10.
Iuchi T, Sugiyama T, Ohira M et al (2018) Clinical significance of the 2016 WHO classification in Japanese patients with gliomas. Brain Tumor Pathol 35:71–80CrossRef
11.
Cimino PJ, Zager M, McFerrin L et al (2017) Multidimensional scaling of diffuse gliomas: application to the 2016 World Health Organization classification system with prognostically relevant molecular subtype discovery. Acta Neuropathol Commun 5:39CrossRef
12.
Tabouret E, Nguyen AT, Dehais C et al (2016) Prognostic impact of the 2016 WHO classification of diffuse gliomas in the French POLA cohort. Acta Neuropathol 132:625–634CrossRef
13.
Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRef
14.
Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157PubMedPubMedCentral
15.
Huang Y-q, Liang C-h, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRef
16.
Kickingereder P, GötzMuschelli MJ et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRef
17.
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762CrossRef
18.
Napel S, Mu W, Jardim-Perassi BV, Aerts H, Gillies RJ (2018) Quantitative imaging of cancer in the postgenomic era: radio(geno)mics, deep learning, and habitats. Cancer 124:4633–4649CrossRef
19.
20.
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRef
21.
Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118CrossRef
22.
Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410CrossRef
23.
Chang P, Grinband J, Weinberg BD et al (2018) Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol 39:1201–1207CrossRef
24.
Liang S, Zhang R, Liang D et al (2018) Multimodal 3D DenseNet for IDH genotype prediction in gliomas. Genes (Basel) 9
25.
26.
Lu CF, Hsu FT, Hsieh KL et al (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436CrossRef
27.
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRef
28.
Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J Roy Stat Soc: Ser B (Methodol) 58:267–288
29.
Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, University of National Taiwan: 1–12.
30.
Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:1140–1152CrossRef
31.
He K, Zhang, X., Ren, S. and Sun, J. (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition:770–778
32.
Zhou B, et al (2016) Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
33.
Krzywinski M, Schein J, Birol I et al (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19:1639–1645CrossRef
34.
Zhang B, Chang K, Ramkissoon S et al (2017) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19:109–117CrossRef
35.
Grossmann P, Narayan V, Chang K et al (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688–1697CrossRef
36.
37.
Wang S, Shi J, Ye Z et al (2019) Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 53
38.
Ting DSW, Cheung CY, Lim G et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318:2211–2223CrossRef
39.
Bush NA, Butowski N (2017) The effect of molecular diagnostics on the treatment of glioma. Curr Oncol Rep 19:26CrossRef
40.
Koriyama S, Nitta M, Kobayashi T et al (2018) A surgical strategy for lower grade gliomas using intraoperative molecular diagnosis. Brain Tumor Pathol 35:159–167CrossRef
41.
Wirsching HG, Weller M (2016) The role of molecular diagnostics in the management of patients with gliomas. Curr Treat Options Oncol 17:51CrossRef
Metadata
Title
Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning
Authors
Yiming Li
Dong Wei
Xing Liu
Xing Fan
Kai Wang
Shaowu Li
Zhong Zhang
Kai Ma
Tianyi Qian
Tao Jiang
Yefeng Zheng
Yinyan Wang
Publication date
01-02-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 2/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-021-08237-6

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