Research Progress of Gliomas in Machine Learning

Overview

Journal Cells

Publisher MDPI

Specialties Biochemistry
Biophysics
Cell Biology
Molecular Biology

Date 2021 Nov 27

PMID 34831392

Citations 6

Authors

Yameng Wu

Yu Guo

Jun Ma

Yu Sa

Qifeng Li

Ning Zhang

Affiliations

Soon will be listed here.

Abstract

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.

Citing Articles

AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.

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Artificial intelligence-aided ultrasound in renal diseases: a systematic review.

Liang X, Du M, Chen Z Quant Imaging Med Surg. 2023; 13(6):3988-4001.

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Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review.

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Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network.

Ramesh K, Agarwal P, Ahuja V, Mir B, Yuriy S, Altuwairiqi M Contrast Media Mol Imaging. 2022; 2022:4946154.

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References

Wu S, Li J, Cao M, Yang J, Li Y, Li Y . A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes. BMC Syst Biol. 2016; 10 Suppl 3:71. PMC: 5009532. DOI: 10.1186/s12918-016-0315-y. View

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

McDermott M, Wang S, Marinsek N, Ranganath R, Foschini L, Ghassemi M . Reproducibility in machine learning for health research: Still a ways to go. Sci Transl Med. 2021; 13(586). DOI: 10.1126/scitranslmed.abb1655. View

Danaee P, Ghaeini R, Hendrix D . A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION. Pac Symp Biocomput. 2016; 22:219-229. PMC: 5177447. DOI: 10.1142/9789813207813_0022. View

Rosenblatt F . The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958; 65(6):386-408. DOI: 10.1037/h0042519. View

Mobadersany P, Yousefi S, Amgad M, Gutman D, Barnholtz-Sloan J, Velazquez Vega J . Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018; 115(13):E2970-E2979. PMC: 5879673. DOI: 10.1073/pnas.1717139115. View

Sun S, Dong B, Zou Q . Revisiting genome-wide association studies from statistical modelling to machine learning. Brief Bioinform. 2020; 22(4). DOI: 10.1093/bib/bbaa263. View

Wang J, Zhang B, Yu J, Liu J, Yang M, Zheng S . Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma. Chin Med J (Engl). 2005; 118(15):1278-84. View

Gao X, Wang Y, Wu S, Rui W, Ma D, Duan Y . Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study. Cancer Manag Res. 2020; 12:3191-3201. PMC: 7213892. DOI: 10.2147/CMAR.S244262. View

10.

Czarnek N, Clark K, Peters K, Mazurowski M . Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. J Neurooncol. 2017; 132(1):55-62. DOI: 10.1007/s11060-016-2359-7. View

11.

Zhou G, Zhang J, Su J, Shen D, Tan C . Recognizing names in biomedical texts: a machine learning approach. Bioinformatics. 2004; 20(7):1178-90. DOI: 10.1093/bioinformatics/bth060. View

12.

Rajkomar A, Dean J, Kohane I . Machine Learning in Medicine. N Engl J Med. 2019; 380(14):1347-1358. DOI: 10.1056/NEJMra1814259. View

13.

Hsu J, Chang T, Lee G, Lee T, Chen C . Identification of potential biomarkers related to glioma survival by gene expression profile analysis. BMC Med Genomics. 2019; 11(Suppl 7):34. PMC: 7402580. DOI: 10.1186/s12920-019-0479-6. View

14.

Korfiatis P, Kline T, Coufalova L, Lachance D, Parney I, Carter R . MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys. 2016; 43(6):2835-2844. PMC: 4866963. DOI: 10.1118/1.4948668. View

15.

Chen H, Li C, Zheng L, Lu W, Li Y, Wei Q . A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters. Cancer Med. 2021; 10(8):2774-2786. PMC: 8026951. DOI: 10.1002/cam4.3838. View

16.

Kristensen B, Priesterbach-Ackley L, Petersen J, Wesseling P . Molecular pathology of tumors of the central nervous system. Ann Oncol. 2019; 30(8):1265-1278. PMC: 6683853. DOI: 10.1093/annonc/mdz164. View

17.

Northcott P, Robinson G, Kratz C, Mabbott D, Pomeroy S, Clifford S . Medulloblastoma. Nat Rev Dis Primers. 2019; 5(1):11. DOI: 10.1038/s41572-019-0063-6. View

18.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

19.

Jackson R, Fuller G, Abi-Said D, Lang F, Gokaslan Z, Shi W . Limitations of stereotactic biopsy in the initial management of gliomas. Neuro Oncol. 2001; 3(3):193-200. PMC: 1920616. DOI: 10.1093/neuonc/3.3.193. View

20.

Haubold J, Demircioglu A, Gratz M, Glas M, Wrede K, Sure U . Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric F-FET PET-MRI and MR Fingerprinting. Eur J Nucl Med Mol Imaging. 2019; 47(6):1435-1445. DOI: 10.1007/s00259-019-04602-2. View