M W Wagner1, N Hainc2,3, F Khalvati2, K Namdar2, L Figueiredo4, M Sheng2, S Laughlin2, M M Shroff2, E Bouffet4, U Tabori4, C Hawkins5, K W Yeom6, B B Ertl-Wagner2. 1. From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.) m.w.wagner@me.com. 2. From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.). 3. Department of Neuroradiology (N.H.), Zurich University Hospital, University of Zurich, Zurich, Switzerland. 4. Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.). 5. Paediatric Laboratory Medicine (C.H.), Division of Pathology, The Hospital for Sick Children and Department of Medical Imaging, University of Toronto, Ontario, Canada. 6. Department of Radiology (K.W.Y.), Stanford University School of Medicine, Lucile Packard Children's Hospital, Palo Alto, California.
Abstract
BACKGROUND AND PURPOSE: B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS: In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS: The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS: Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.
BACKGROUND AND PURPOSE: B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS: In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS: The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS: Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.
Authors: S Vajapeyam; D Brown; A Ziaei; S Wu; G Vezina; J S Stern; A Panigrahy; Z Patay; B Tamrazi; J Y Jones; S S Haque; D S Enterline; S Cha; B V Jones; K W Yeom; A Onar-Thomas; I J Dunkel; M Fouladi; J R Fangusaro; T Y Poussaint Journal: AJNR Am J Neuroradiol Date: 2022-02-24 Impact factor: 3.825
Authors: A Trasolini; C Erker; S Cheng; C Crowell; K McFadden; R Moineddin; M A Sargent; D Mata-Mbemba Journal: AJNR Am J Neuroradiol Date: 2022-07-21 Impact factor: 4.966