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DOI: 10.1055/s-0043-1767786
A Comparison of Machine Learning Models for Survival Prediction of Patients with Glioma Using Radiomic Features from MRI Scans
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Abstract
Background Glioma is a primary, malignant, highly aggressive brain tumor, with patients having an average life expectancy of 14 to 16 months after diagnosis. Magnetic resonance imaging (MRI) scans of these patients can be used to extract and analyze quantifiable features with potential clinical significance. We hypothesize that there is a correlation between radiomic features extracted from MRI scans and survival. Along with clinical data, the radiomic features could be used in survival prediction of patients, providing beneficial information for clinicians to design personalized treatment plans.
Methods In our study, we have utilized 3D Slicer for tumor segmentation and feature extraction and performed survival prediction of patients with glioma using four different machine learning models.
Results and Conclusion Among the models compared, we have achieved a maximum prediction accuracy of 64.4% using the k-nearest neighbors model, which was trained and tested on a combination of clinical data and radiomic features extracted from MRI images provided in the BraTS 2020 dataset.
Keywords
survival prediction - feature extraction - MRI - naïve Bayes - random forest - k-nearest neighborsAuthor Contributions
The idea was conceived by all the authors. S.K. and M.M. performed feature extraction using the 3D Slicer software. S.S. performed the data visualization, coding for the four machine learning models, and implementation of survival prediction using R programming. The manuscript was prepared by all the authors. J.C. supervised the study and also reviewed and finalized the manuscript.
* These authors have contributed equally to the work.
Publikationsverlauf
Artikel online veröffentlicht:
28. April 2023
© 2023. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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