Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study

Overview

Journal Cancers (Basel)

Publisher MDPI

Date 2025 Jan 25

PMID 39858104

Authors

Melike Bilgin

Sabriye Sennur Bilgin

Burak Han Akkurt

Walter Heindel

Manoj Mannil

Manfred Musigmann

Affiliations

Soon will be listed here.

Abstract

Background/objectives: In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images. The aim of our proof-of-concept study is to investigate whether machine learning-based tumor diagnosis is also possible using CT images.

Methods: We investigate the differentiability of histologically confirmed low-grade and high-grade gliomas. Three conventional machine learning algorithms and a neural net are tested. In addition, we analyze which of the common imaging methods (MRI or CT) appears to be best suited for the diagnostic question under investigation when machine learning algorithms are used. For this purpose, we compare our results based on CT images with numerous studies based on MRI scans.

Results: Our best-performing model includes six features and is obtained using univariate analysis for feature preselection and a Naive Bayes approach for model construction. Using independent test data, this model yields a mean AUC of 0.903, a mean accuracy of 0.839, a mean sensitivity of 0.807 and a mean specificity of 0.864.

Conclusions: Our results demonstrate that low-grade and high-grade gliomas can be differentiated with high accuracy using machine learning algorithms, not only based on the usual MRI scans, but also based on CT images. In the future, such CT-image-based models can help to further accelerate brain tumor diagnostics and to reduce the number of necessary biopsies.

References

Liu X, Tian W, Kolar B, Yeaney G, Qiu X, Johnson M . MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol. 2011; 13(4):447-55. PMC: 3064693. DOI: 10.1093/neuonc/noq197. View

Li Y, Ammari S, Lawrance L, Quillent A, Assi T, Lassau N . Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion. Cancers (Basel). 2022; 14(7). PMC: 8997070. DOI: 10.3390/cancers14071778. View

Perez A, Huse J . The Evolving Classification of Diffuse Gliomas: World Health Organization Updates for 2021. Curr Neurol Neurosci Rep. 2021; 21(12):67. DOI: 10.1007/s11910-021-01153-8. View

Shaw E, Berkey B, Coons S, Bullard D, Brachman D, Buckner J . Recurrence following neurosurgeon-determined gross-total resection of adult supratentorial low-grade glioma: results of a prospective clinical trial. J Neurosurg. 2008; 109(5):835-41. PMC: 3833272. DOI: 10.3171/JNS/2008/109/11/0835. View

Wesseling P, Capper D . WHO 2016 Classification of gliomas. Neuropathol Appl Neurobiol. 2017; 44(2):139-150. DOI: 10.1111/nan.12432. View

Wijnenga M, Van der Voort S, French P, Klein S, Dubbink H, Dinjens W . Differences in spatial distribution between WHO 2016 low-grade glioma molecular subgroups. Neurooncol Adv. 2021; 1(1):vdz001. PMC: 8051437. DOI: 10.1093/noajnl/vdz001. View

Wang T, Mehta M . Low-Grade Glioma Radiotherapy Treatment and Trials. Neurosurg Clin N Am. 2018; 30(1):111-118. DOI: 10.1016/j.nec.2018.08.008. View

van den Bent M . Anaplastic oligodendroglioma and oligoastrocytoma. Neurol Clin. 2007; 25(4):1089-109, ix-x. DOI: 10.1016/j.ncl.2007.07.013. View

Hirtz A, Rech F, Dubois-Pot-Schneider H, Dumond H . Astrocytoma: A Hormone-Sensitive Tumor?. Int J Mol Sci. 2020; 21(23). PMC: 7730176. DOI: 10.3390/ijms21239114. View

10.

Yan J, Zhang S, Sun Q, Wang W, Duan W, Wang L . Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study. Lab Invest. 2021; 102(2):154-159. DOI: 10.1038/s41374-021-00692-5. View

11.

Hashido T, Saito S, Ishida T . Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging. J Comput Assist Tomogr. 2021; 45(4):606-613. DOI: 10.1097/RCT.0000000000001180. View

12.

Delgado A, Falk Delgado A . Discrimination between primary low-grade and high-grade glioma with C-methionine PET: a bivariate diagnostic test accuracy meta-analysis. Br J Radiol. 2017; 91(1082):20170426. PMC: 5965775. DOI: 10.1259/bjr.20170426. View

13.

Claus E, Walsh K, Wiencke J, Molinaro A, Wiemels J, Schildkraut J . Survival and low-grade glioma: the emergence of genetic information. Neurosurg Focus. 2015; 38(1):E6. PMC: 4361022. DOI: 10.3171/2014.10.FOCUS12367. View

14.

McNeill K . Epidemiology of Brain Tumors. Neurol Clin. 2016; 34(4):981-998. DOI: 10.1016/j.ncl.2016.06.014. View

15.

Nayak L, Reardon D . High-grade Gliomas. Continuum (Minneap Minn). 2017; 23(6, Neuro-oncology):1548-1563. DOI: 10.1212/CON.0000000000000554. View

16.

Ari A, Akkurt B, Musigmann M, Mammadov O, Blomer D, Kasap D . Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics. Sci Rep. 2022; 12(1):5915. PMC: 8993885. DOI: 10.1038/s41598-022-09945-9. View

17.

Zhang X, Tian Q, Wang L, Liu Y, Li B, Liang Z . Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI. J Magn Reson Imaging. 2018; 48(4):916-926. DOI: 10.1002/jmri.25960. View

18.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

19.

Maskani M, Abbasi S, Etemad-Rezaee H, Abdolahi H, Zamanpour A, Montazerabadi A . Grading of Gliomas by Contrast-Enhanced CT Radiomics Features. J Biomed Phys Eng. 2024; 14(2):151-158. PMC: 11016825. DOI: 10.31661/jbpe.v0i0.2306-1628. View

20.

Choi R, Coyner A, Kalpathy-Cramer J, Chiang M, Campbell J . Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020; 9(2):14. PMC: 7347027. DOI: 10.1167/tvst.9.2.14. View