Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Eichinger, Paul; Alberts, Esther; Delbridge, Claire; Trebeschi, Stefano; Valentinitsch, Alexander; Bette, Stefanie; Huber, Thomas; Gempt, Jens; Meyer, Bernhard; Schlegel, Juergen; Zimmer, Claus; Kirschke, Jan; Menze, Bjoern; Wiestler, Benedikt
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-13679-4
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predictIDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predictIDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC = 0.921) in the training and 95% (19/20; AUC = 0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predictsIDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.