The success of machine learning algorithms developed with radiomic features obtained from preoperative contrast-enhanced MRI in the prediction of short-term survival in patients with glioblastoma
Abstract
Purpose: This study aimed to evaluate the predictability of survival in patients with glioblastoma using a machine learning (ML) model developed with tissue analysis features obtained through preoperative post-contrast T1-weighted images(T1WI).
Materials and Methods: The radiomic features of tumors were obtained from postcontrast T1WI of 60 glioblastoma patients. Radiomic properties, density, shape, and textural properties obtained from six matrices were included in the analysis. The patients' three- and six-month survival rates were recorded. Five different ML algorithms were applied to create predictive models [random forest, neural network, linear discriminant analysis(LDA), stochastic gradient descent (SGD), and support vector machine(SMV)].
Results: The mean survival time of the patients was 295.4 days, and the median value was 211.5 (17-1357) days. Among the models developed for three- and six-month survival prediction, the highest success was obtained from the LDA algorithm, in which the AUC values were calculated as 0.88 and 0.78, respectively.
Conclusion: Using ML techniques, the success of predicting imaging-based patient survival was very high. With the development and widespread adoption of these techniques, ML models will be useful in deciding on treatment according to survival prediction in glioblastoma.
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