Abstract
BackgroundThe 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.MethodsThirty-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.ResultsNineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists’ assessment.ConclusionsMachine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.
Highlights
The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge
We aimed to evaluate the diagnostic power of machine-learning based on T1 T1-weighted contrast-enhanced image (CE) and fluid attenuated inversion recovery (FLAIR) imaging radiomics in comparison with the radiologists’ performance in differentiating ODG2 from ODG3
10/19 (52.6%) of ODG2 and 10/17 (58.8%) of ODG3 situated in the frontal lobe, Table 1 Clinical characteristics and magnetic resonance imaging (MRI) features of patients
Summary
The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. Patients with low-grade (ODG2) are slightly younger than those with high-grade, anaplastic tumors (ODG3) [2]. Calcification [4, 5] and the cortical-subcortical location [5, 6], most commonly in the frontal lobe [4], are regarded as the characteristic features of ODGs. In contrast to other low-grade gliomas (LGG), minimal to moderate enhancement and moderately increased perfusion are commonly seen in ODGs, making the differentiation of OGD2 from OGD3 difficult. ODG3 often shares the imaging features with ODG2 on conventional MRI, leading to unreliable tumor grade prediction. A new medical imaging diagnostic strategy for differentiation of ODG2 from ODG3 needs to be developed
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