Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is 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) were evaluated by an AI segmentation tool, comparatively evaluating tumor heterogeneity and peripheral edema. Results: We found that GBMs are less edematous than BMs (p = 0.04) but have more internal necrosis (p = 0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the highest grade of edema (p = 0.01). Compared with the ellipsoidal method of volume calculation, the AI machine obtained greater values when 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 a conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheral edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation.

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.

Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis.

Background/Objectives: Our study aims to identify potential new MRI features of brain metastases (BMs) that could be further used in overall survival (OS) assessment. Methods: A total of 109 patients with BMs were included. Kaplan–Meier analysis, the log-rank test, and Cox Regression were implemented in the survival analysis. The first ten significant features were incorporated into four distinct machine learning (ML) algorithms to predict six-month survival. Results: Survival analysis revealed that multiple brain lesions and synchronous presentation were associated with a poor prognostic value (HR > 1; p = 0.01, p = 0.02). Other features demonstrated a protective effect on OS including the absence of extracranial lesions (HR < 1, p = 0.04) and the presence of solid enhancement (HR < 1, p < 0.05). In this observational cohort, treatment was associated with longer OS—including surgery, gamma knife radiosurgery, whole brain radiation therapy, and chemotherapy—compared to best supportive care (HR < 1, p < 0.005); these treatment-related hazard ratios are not interpreted causally. The shallow Neural Networks model was the top-performing ML model, achieving an AUC of 0.93 (CI = 0.89–0.97). According to the Shapley Additive Explanations analysis, the solid enhancement type had a positive impact on OS, whereas a higher number of lesions, larger volumes and a cystic morphology were associated with negative outcomes. Conclusions: Our results confirm that including morphological MRI features of BMs in the prediction of OS significantly contributes to the enhancement of ML algorithms’ prediction and discriminatory capacity.

Background/Objectives: Our study aims to identify potential new MRI features of brain metastases (BMs) that could be further used in overall survival (OS) assessment. Methods: A total of 109 patients with BMs were included. Kaplan-Meier analysis, the log-rank test, and Cox regression were implemented in the survival analysis. Signifi-cant features were subsequently incorporated into four distinct machine learning (ML) algorithms to predict six-month survival. Results: Survival analysis revealed that multiple brain lesions, synchronous presentation, and restricted diffusion were associ-ated with a poor prognostic value (HR > 1; p = 0.01, p = 0.02, and p < 0.005, respective-ly). Other features demonstrated a protective effect OS: the absence of extracranial le-sions (HR < 1, p = 0.04), and the presence of peripheral edema and solid enhancement (HR < 1, p < 0.0005). All treatment protocols—including surgery, gamma knife radio-surgery, whole brain radiation therapy, and chemotherapy - showed significantly bet-ter survival rates compared to best supportive care (HR < 1, p < 0.005). The Neural Networks was the top-performing ML model, achieving an AUC of 0.93. According to the Shapley Additive Explanations analysis, solid enhancement and radiotherapy had a positive impact on OS, whereas a higher number of lesions, larger volumes, and re-stricted diffusion were associated with negative outcomes. Conclusions: Our results confirm that including morphological MRI features of BMs in the prediction of OS sig-nificantly contributes to the enhancement of ML algorithms prediction and discrimi-natory capacity.

Osteoporosis, a systemic disorder characterized by reduced bone density and microarchitectural deterioration, significantly affects bone health, including the mandibular and periodontal structures. This study aimed to evaluate the relationship between osteoporosis and periodontal pathology using imaging techniques in patients diagnosed with osteoporosis. Material and methods: The retrospective, descriptive study included 63 patients diagnosed with osteoporosis and other systemic conditions. Periodontal status was assessed clinically and paraclinically using the EGOHID evaluation system, while bone density was evaluated radiographically using indices such as the Mandibular Cortical Index (MCI), Mental Index (MI), Panoramic Mandibular Index (PMI), and Mandibular Cortical Width (MCW). Results: Results revealed significant periodontal involvement, with 42.9% of patients exhibiting moderate chronic marginal periodontitis and 46% severe periodontitis, more pronounced in the maxillary arch than the mandibular. Radiographic evaluation demonstrated that 58.7% of patients with osteoporosis had a slightly eroded mandibular cortex (C2), while 34.9% showed severe erosion (C3). The average cortical thickness (MCW) was significantly reduced in osteoporotic patients compared to non-osteoporotic individuals, highlighting its potential as a screening indicator. Conclusions: Osteoporosis is strongly associated with significant periodontal alterations and mandibular cortical resorption. These findings underline the importance of integrating bone health assessments into periodontal evaluations, emphasizing the role of imaging as a non-invasive screening tool for osteoporosis.