Purpose: This study aimed to develop a repeatable MRI-based machine learning model to differentiate between low-grade gliomas (LGGs) and glioblastoma (GBM) and provide more clinical information to improve treatment decision-making.
Methods: Preoperative MRIs of gliomas from The Cancer Imaging Archive (TCIA)-GBM/LGG database were selected. The tumor on contrast-enhanced MRI was segmented. Quantitative image features were extracted from the segmentations. A random forest classification algorithm was used to establish a model in the training set. In the test phase, a random forest model was tested using an external test set. Three radiologists reviewed the images for the external test set. The area under the receiver operating characteristic curve (AUC) was calculated. The AUCs of the radiomics model and radiologists were compared.
Results: The random forest model was fitted using a training set consisting of 142 patients [mean age, 52 years ± 16 (standard deviation); 78 men] comprising 88 cases of GBM. The external test set included 25 patients (14 with GBM). Random forest analysis yielded an AUC of 1.00 [95% confidence interval (CI): 0.86-1.00]. The AUCs for the three readers were 0.92 (95% CI 0.74-0.99), 0.70 (95% CI 0.49-0.87), and 0.59 (95% CI 0.38-0.78). Statistical differences were only found between AUC and Reader 1 (1.00 vs. 0.92, respectively; p = 0.16).
Conclusion: An MRI radiomics-based random forest model was proven useful in differentiating GBM from LGG and showed better diagnostic performance than that of two inexperienced radiologists.
Keywords: MRI; gliomas; histological grade; machine learning; radiomics.
Copyright © 2022 Zhang, Zhang, Pan, Dong, Li, Chen, Wang and Ji.