Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recov...

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Published in:BMC neurology 2020-02, Vol.20 (1), p.48-48, Article 48
Main Authors: Zhao, Sha-Sha, Feng, Xiu-Long, Hu, Yu-Chuan, Han, Yu, Tian, Qiang, Sun, Ying-Zhi, Zhang, Jie, Ge, Xiang-Wei, Cheng, Si-Chao, Li, Xiu-Li, Mao, Li, Shen, Shu-Ning, Yan, Lin-Feng, Cui, Guang-Bin, Wang, Wen
Format: Article
Language:English
Subjects:
Age
Algorithms
Brain cancer
Diagnosis
Diagnostic imaging
Edema
Gadodiamide
Itk protein
Learning algorithms
Machine learning
Magnetic resonance imaging
Magnetic resonance imaging (MRI)
Medical imaging equipment
Necrosis
Oligodendroglioma
Oligodendrogliomas
Patients
Practice
Public health
Radiologists
Radiomics
Random forest (RF)
Tumors
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Summary:The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P 
ISSN:1471-2377
1471-2377
DOI:10.1186/s12883-020-1613-y