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 2.25% increase in sample size.
Results: Two-hundred-twenty patients (mean age 8.53 ± 4.94 years, 114 males, 67% BRAF fusion) were included in the training dataset and 31 patients (mean age 7.97 ± 6.20 years, 18 males, 77% BRAF fusion) in the independent dataset. NN1 (100:20:2) yielded the highest area under the receiver operating characteristic curve (AUC). It predicted BRAF status with a mean AUC of 0.85, 95% CI (0.83, 0.87) using 60% of the training data and with mean AUC of 0.83, 95% CI (0.82, 0.84) on the independent validation data set.
Conclusion: Neural nets have the highest AUC to predict BRAF status compared to Random Forest and XG Boost. The highest AUC for training and independent data was reached at 60% of the training population (132 patients).
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Prof. Elhadi Miskeen
Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia
Ahmed Hussien Alshewered
University of Basrah College of Medicine, Iraq
Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh
Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis
Fava Maria Giovanna
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