BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning. METHODS: Our dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models. RESULTS: The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging. CONCLUSION: Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups.
BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning. METHODS: Our dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models. RESULTS: The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging. CONCLUSION: Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups.
Authors: Sohil H Patel; Laila M Poisson; Daniel J Brat; Yueren Zhou; Lee Cooper; Matija Snuderl; Cheddhi Thomas; Ana M Franceschi; Brent Griffith; Adam E Flanders; John G Golfinos; Andrew S Chi; Rajan Jain Journal: Clin Cancer Res Date: 2017-07-27 Impact factor: 12.531
Authors: Y W Park; K Han; S S Ahn; S Bae; Y S Choi; J H Chang; S H Kim; S-G Kang; S-K Lee Journal: AJNR Am J Neuroradiol Date: 2017-11-09 Impact factor: 3.825
Authors: Spyridon Bakas; Hamed Akbari; Aristeidis Sotiras; Michel Bilello; Martin Rozycki; Justin S Kirby; John B Freymann; Keyvan Farahani; Christos Davatzikos Journal: Sci Data Date: 2017-09-05 Impact factor: 6.444
Authors: Chandan Ganesh Bangalore Yogananda; Bhavya R Shah; Frank F Yu; Marco C Pinho; Sahil S Nalawade; Gowtham K Murugesan; Benjamin C Wagner; Bruce Mickey; Toral R Patel; Baowei Fei; Ananth J Madhuranthakam; Joseph A Maldjian Journal: Neurooncol Adv Date: 2020-07-17
Authors: James H Cole; Rudra P K Poudel; Dimosthenis Tsagkrasoulis; Matthan W A Caan; Claire Steves; Tim D Spector; Giovanni Montana Journal: Neuroimage Date: 2017-07-29 Impact factor: 6.556
Authors: Johannes Haubold; René Hosch; Vicky Parmar; Martin Glas; Nika Guberina; Onofrio Antonio Catalano; Daniela Pierscianek; Karsten Wrede; Cornelius Deuschl; Michael Forsting; Felix Nensa; Nils Flaschel; Lale Umutlu Journal: Cancers (Basel) Date: 2021-12-08 Impact factor: 6.639