Background/Objectives: Glioblastoma (GBM) is an aggressive and lethal primary brain tumor with a poor prognosis, with a 5-year survival rate of approximately 5%. Despite advances in oncologic treatments, including surgery, radiotherapy, and chemotherapy, survival outcomes have remained stagnant, largely due to the failure of conventional therapies to address the tumor’s inherent heterogeneity. Radiomics, a rapidly emerging field, provides an opportunity to extract features from MRI scans, offering new insights into tumor biology and treatment response. This study evaluates the potential of delta radiomics, the study of changes in radiomic features over time in response to treatment or disease progression, exploring the potential of delta radiomics to track temporal radiation changes in tumor morphology and microstructure. Methods: A cohort of 50 female CD1 nude mice was injected intracranially with G7 glioblastoma cells and divided into irradiated (IR) and non-irradiated (non-IR) groups. MRI scans were performed at baseline (week 11) and post-radiation (weeks 12 and 14), and radiomic features, including shape, histogram, and texture parameters, were extracted and analyzed to capture radiation-induced changes. The most robust features were those identified through intra-observer reproducibility assessment, ensuring reliability in feature selection. A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. Results: Our findings demonstrate that delta radiomics effectively captured significant temporal variations in tumor characteristics. Delta radiomics features exhibited distinct patterns across different time points in the IR group, enabling machine learning models to achieve a high accuracy. Conclusions: Delta radiomics offers a robust, non-invasive method for monitoring the treatment of glioblastoma (GBM) following radiation therapy. Future research should prioritize the application of MRI delta radiomics to effectively capture short-term changes resulting from intratumoral radiation effects. This advancement has the potential to significantly enhance treatment monitoring and facilitate the development of personalized therapeutic strategies.

Background: Vascular dysfunction and neurovascular inflammation are increasingly recognized as contributors to Alzheimer’s disease (AD) pathophysiology, particularly through interactions with tau-related neurodegeneration. Endothelial adhesion molecules, including vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1), play key roles in blood–brain barrier regulation and immune-vascular crosstalk, yet their relevance to long-term disease progression and established AD biomarkers remains incompletely understood. Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we examined associations between baseline cerebrospinal fluid (CSF) levels of VCAM-1 and ICAM-1 and clinical progression, CSF biomarkers, neuroimaging measures, and cognitive outcomes over up to 10 years of follow-up. This study included 294 participants (87 cognitively normal, 129 with mild cognitive impairment, and 78 with AD). Multivariable logistic regression was used to assess associations with diagnostic progression, and linear regression models examined relationships with baseline and longitudinal measures of tau, amyloid-β, hippocampal volume, Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) metabolism, and cognition. Models were adjusted for age, sex, apolipoprotein E epsilon 4 (APOE ε4) status, baseline diagnosis, and baseline CSF amyloid-β, with false discovery rate correction applied for multiple comparisons. Results: Baseline CSF VCAM-1 and ICAM-1 levels did not differ across diagnostic groups. However, higher baseline levels of both markers were nominally associated with increased odds of disease progression. Notably, ICAM-1 showed a strong and robust association with baseline CSF phosphorylated tau, which remained significant after multiple-comparison correction. VCAM-1 was also associated with tau pathology, though this did not survive correction. Neither marker was associated with baseline or longitudinal changes in hippocampal volume, FDG-PET metabolism, or cognitive performance. Conclusion: CSF VCAM-1 and ICAM-1 appear to reflect neurovascular inflammatory processes linked to tau pathology rather than markers of clinical stage or longitudinal neurodegeneration. These findings support a role for endothelial activation in AD pathophysiology and highlight vascular–immune mechanisms as potential contributors to tau-related disease vulnerability.

Background: The role of cerebrospinal fluid (CSF) transforming growth factor β (TGF-β) isoforms in Alzheimer’s disease (AD) remains unclear. We examined associations of CSF TGF-β1, TGF-β2, and TGF-β3 with AD biomarkers, neurodegeneration, cognition, and clinical progression. Methods: In 294 ADNI participants with baseline CSF TGF-β measurements, adjusted regression, and mixed-effect models were used to evaluate associations with CSF biomarkers, neuroimaging, cognitive outcomes, and conversion. False-discovery-rate correction was applied. Results: Higher baseline TGF-β1 was associated with higher CSF total tau (β = 52.68 pg/mL per 1 SD increase; q < 0.001) and p-tau (β = 5.68 pg/mL; q < 0.001). Higher TGF-β2 was associated with faster hippocampal volume loss (β = −45.42 mm³/year; q < 0.001). No isoform was robustly associated with FDG-PET decline, cognitive decline, clinical conversion, or time to conversion. Conclusions: CSF TGF-β1 and TGF-β2 show distinct associations with tau-related pathology and hippocampal neurodegeneration, respectively, but do not appear to be prognostic biomarkers of clinical progression in AD.