Uri Tabori

Publications

• Research Article   
  Dataset size sensitivity analysis of machine learning classifiers to differentiate molecular markers of paediatric low-grade gliomas based on MRI
  Author(s): Matthias W. Wagner*, Khashayar Namdar, Abdullah Alqabbani, Nicolin Hainc, Liana Nobre Figuereido, Min Sheng, Manohar M Shroff, Eric Bouffet, Uri Tabori, Cynthia Hawkins, Michael Zhang, Kristen W. Yeom, Farzad Khalvati and Birgit B. Ertl-Wagner
  
  Objectives: BRAF status has important implications for prognosis and therapy of Pediatric Low-Grade Gliomas (pLGG). Machine Learning (ML) approaches can predict BRAF status of pLGG on pre-therapeutic brain MRI, but the impact of training data sample size and type of ML model is not established. Methods: In this bi-institutional retrospective study, 251 pLGG FLAIR MRI datasets from 2 children’s hospitals were included. Radiomics features were extracted from tumor segmentations and five models (Random Forest, XGBoost, Neural Network (NN) 1 (100:20:2), NN2 (50:10:2), NN3 (50:20:10:2)) were tested to classify them. Classifiers were cross-validated on data from institution 1 and validated on data from institution 2. Starting with 10% of the training data, models were cross-validated using a 4-fold approach at every step with an additional 2.. Read More»
  DOI: 10.4172/1896-8961.16.S1.002

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