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Editorial for “Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features”

Editorial for “Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features” Gliomas, the most common brain tumors, are histopathologically graded as WHO grade I–IV with an increasing degree of malignancy. The grades are guidance for prognosis and treatment. However, in spite of identical morphologic criteria, tumors show highly different outcomes. Tumor aggressiveness is primarily influenced by its molecular genetic composition, which does not show up in conventional imaging features. Therefore, the most recent 2016 WHO classification of tumors is not only based on histopathological appearance but also on molecular parameters, among them isocitrate dehydrogenase (IDH) mutation status. The refined classification of tumors allows a more specific tumor treatment. MR as the modality of choice for detection and identification of intracranial tumors is commonly applied with imaging protocols that generally include T2-weighted (T2w), T2-fluidattenuated inversion recovery (FLAIR), preand postcontrast T1w images, and diffusion-weighted imaging (DWI). 4 In general, images are analyzed based on visual inspection of position of the tumor, permeability of the blood–brain barrier as judged from contrast enhancement, lesion shape, and heterogeneity. More advanced techniques such as MR spectroscopy, susceptibility-weighted imaging (SWI), or quantitative mapping indicators such as the apparent diffusion coefficient (ADC) are used, if they are available. As a consequence of the impact of genomic alterations in gliomas, there is an ongoing quest for noninvasive analysis techniques that apply imaging markers, which relate imaging features with tumor type and subtype. A specific aspect is the determination of IDH mutation status, which correlates with the aggressiveness of low-grade gliomas. A direct method is MR spectroscopy, which directly shows metabolites that indicate the mutation status. Compared to MR spectroscopy the production of images is technically less demanding. Traditional visual inspection of MR images allows glioma characterization to a certain extent, but it does not reveal the IDH status directly. There are specific imaging features that correlate with altered metabolic processes of the tumor and thus contain the desired information. Examples are relative cerebral blood volume (rCBV) imaging and mismatch of signal intensity distribution of T2w and FLAIR images (T2FLAIR mismatch). Also, subtle imaging features of common diagnostic protocols may indicate the IDH mutation status. This can be ascertained with the application of machinelearning techniques. It has been shown that features already exploited in image processing in the fields of satellite image analysis actually correlate with IDH status. Sensitivity and specificity depends on the right choice of a feature combination, and correct training of learning algorithms. The applicability of machine learning has been shown, for example, by Yu et al with a quantitative radiomics approach on 140 patients with grade II gliomas, applying two machine-learning algorithms (support vector machine [SVM] and AdaBoost) on 110 selected imaging features of the tumor as location, intensity, shape, texture, and wavelet features. This resulted in quite reliable discrimination of IDH1 mutation vs. IDH wildtype, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.86, based on T2 FLAIR imaging. A number of other groups present similar results on lowand high-grade gliomas with slightly different settings (see references in Yu et al and Peng et al in this issue). The low number of patients—typically about 100—is far from “Big Data”; thus, deep-learning algorithms have not been applied. The present work aims to find imaging markers for IDH mutation based on commonly available imaging methods. The authors repeated a multiparametric radiomics evaluation on 105 patients with grade II–IV gliomas with a setting slightly different from previous studies, based on postcontrast T1w, T2w, and arterial spin labeling (ASL) imaging, analyzed separately as well as all three combined. Testing three different feature selection methods, they showed similarly good discrimination. One setting was optimal, but there were only moderate differences between the tested settings. While the repeated success of the machine-learning approach, even with different settings, confirms that the chosen SVM methods do work in principle, several questions need to be considered. The main limitation is the fact that