Artificial Intelligence-Based MRI Segmentation for the Differential Diagnosis of Single Brain Metastasis and Glioblastoma

Background/Objectives: Glioblastoma (GBM) and brain metastases (BMs) are both frequent brain lesions. Their distinction is crucial for suitable therapeutic and follow-up decisions, but difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination of conventional and advanced MRI techniques can overcome this issue. Methods: We retrospectively selected 78 patients with confirmed GBM (39) and single BM (39), with conventional MRI investigations, consisting of T2W FLAIR and CE T1W acquisitions. The MRI images (DICOM) have been evaluated by an AI segmentation tool, comparatively evaluating the tumor heterogeneity and peripheric edema. Results: We found that GBM are less edematous than BM (p=0.04), but have more internal necrosis (p=0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the higher grade of edema (p=0.01). Compared to the ellipsoidal method of volume calculation, the AI machine obtained greater values in measuring lesions of the occipital and temporal lobes (p=0.01). Conclusions: Although extremely useful in radiomics analysis, automated segmentation applied alone could effectively differentiate GBM and BM on conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheric edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation.