Although magnetic resonance imaging (MRI) is the main tool for differentiating between the two types of tumors, both GBM and SBM may show marked peritumoral edema and similar contrast-enhancement patterns on routine MRI, leading to great challenges in identification. Glioblastoma multiforme (GBM) and solitary brain metastases (SBM) are the most common malignant brain tumors, and their correct identification is key for further diagnosis and treatment. Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM. The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUC training:0.845, AUC test:0.714), solid tumor (AUC training:0.852, AUC test:0.821), peritumoral edema (AUC training:0.817, AUC test:0.762), and ABV (AUC training:0.834, AUC test:0.779). Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Like anything in math, there is more than one good way to make a connection.We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). When I posted this video on my Instagram feed, one teacher pressed me for the connection to the keep change flip (aka: multiplying by the reciprocal) standard algorithm. There is even room for one more! So all of the green bars (1 whole) can fit into the blue space and 1 more bar out of the 3 (1/3). By creating a common denominator, it will be easier to see how many will fit.īy creating the common denominator 6, we can then see that all 3 of our green bars will fit into the space taken up by our blue bars. In our first example (2/3)÷(1/2), we're asking, "How many 1/2s fit into 2/3?" It would be easier to answer this question if our fractions were broken into the same number of parts. Division asks, "How many of these fit into that?" For 10 divided by 2, for example, we're asking, "How many 2's fit into 10?" We ask the same question when we divide fractions, it's just a little harder to see.
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