A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis

Abstract

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).

CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone

“…The authors [ 29 ] considered these parameters a reflection of the inhomogeneity exhibited by HGGs’ PZ, an observation that is in accordance with our current and previous [ 28 ] results. Recently, a multiparametric MR-based RadioFusionOmics model [ 30 ] using combined texture parameters extracted from the tumoral and peritumoral zone was able to differentiate the two lesion categories with 85.5% accuracy, 85.6% Se, and 85.3% Sp. Interestingly, none of the above-mentioned studies extracted shape-based textural features in an attempt to differentiate between the two entities.…”

Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

“…With new machine learning algorithms (ML), radiomics analysis could be more precise, accurate, and convenient for clinical reports [1][2][3][4]6]. Predictive model can be created based on the unique patterns found in the data by using these algorithms [6][7][8][9][10].…”

“…Many previous studies showed that combined conventional MRI (cMRI), diffusion-weighted imaging (DWI), and 18F-FDG-PET images to establish different radiomics models to differentiate MET from GBM and found that the integrated model based on cMRI, DWI, and 18F-FDG-PET had the best discriminatory power. In contrast, advanced sequences like DWI are not widely available in the clinic as cMRI [3,12,20]. Consequently, the radiomics literature shows that different classifiers have different outputs [1][2][3][4][5][6].…”

“…In contrast, advanced sequences like DWI are not widely available in the clinic as cMRI [3,12,20]. Consequently, the radiomics literature shows that different classifiers have different outputs [1][2][3][4][5][6]. Choosing the best model is complex [3][4][5][6].…”

“…Consequently, the radiomics literature shows that different classifiers have different outputs [1][2][3][4][5][6]. Choosing the best model is complex [3][4][5][6]. It is necessary to build various models to achieve a more excellent result [3,12,20].…”