Abstract
Objective The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM). Methods A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods. Results In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; p < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities (p < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset). Conclusion Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.
Highlights
Brain metastases (BMs) are the most common tumors of the central nervous system (CNS), with an incidence of approximately 7–14 per 100,000 population [1], whereas glioblastoma (GBM) has an incidence rate of 3.22 per 100,000 population and is the most common malignant primary brain tumor in adults [2]
According to the World Health Organization (WHO) classification of CNS tumors, gliomas can be categorized as grades I–IV based on Journal of Oncology histological characteristics; GBM is categorized as a WHO grade IV glioma, accounting for the majority of gliomas [3]
The standard treatment for BM is stereotactic radiotherapy, while the most effective therapeutic method for GBM is surgery. erefore, an accurate preoperative diagnosis is of significance for surgical planning, determining the extent of resection [6], evaluating the need for neoadjuvant therapy, defining the radiation therapy field, and counseling patients and their families [7]
Summary
Brain metastases (BMs) are the most common tumors of the central nervous system (CNS), with an incidence of approximately 7–14 per 100,000 population [1], whereas glioblastoma (GBM) has an incidence rate of 3.22 per 100,000 population and is the most common malignant primary brain tumor in adults [2]. Systemic malignancy was present in approximately 3% of high-grade glioma cases [9], and multifocal lesions accounted for up to 20% of GBM cases in some reports [10]. As both BM and GBM can present with contrast-enhancing and necrotic areas, they often present a similar anatomical appearance on MRI [8]
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