The most common primary brain tumor is glioblastoma (GBM), yet the current therapeutic options for this disease are not promising. Although immunotherapeutic techniques have shown poor success in GBM thus far despite efforts, new developments provide optimism. One of these developments is chimeric antigen receptor (CAR)-T cell treatment, which includes removing and genetically modifying autologous T cells to produce a receptor that targets a GBM antigen before reintroducing the cells into the patient’s body. A number of preclinical studies have produced encouraging results, which have led to the start of clinical trials assessing these CAR-T cell treatments for GBM and other brain tumors. Although results in tumors such as diffuse intrinsic pontine gliomas and lymphomas have been promising, preliminary findings in GBM have not produced any clinical benefits. The paucity of particular antigens in GBM, their inconsistent expression patterns, and the possible immunoediting-induced loss of these antigens after antigen-targeted therapy are some possible causes for this discrepancy. The goal of this systematic literature review is to assess potential approaches for creating CAR-T cells that are more effective for this indication, as well as the clinical experiences that are already being had with CAR-T cell therapy in GBM. Up until 9 May 2024, a thorough search was carried out across the three main medical databases: PubMed, Web of Science, and Scopus. Relevant Medical Subject Heading (MeSH) terms and keywords associated with “glioblastoma”, “CAR-T”, “T cell therapy”, “overall survival”, and “progression free survival” were employed in the search approach. Preclinical and clinical research on the application of CAR-T cells as a therapeutic approach for GBM are included in the review. A total of 838 papers were identified. Of these, 379 articles were assessed for eligibility, resulting in 8 articles meeting the inclusion criteria. The included studies were conducted between 2015 and 2023, with a total of 151 patients enrolled. The studies varied in CAR-T cell types. EGFRvIII CAR-T cells were the most frequently investigated, used in three studies (37.5%). Intravenous delivery was the most common method of delivery (62.5%). Median OS ranged from 5.5 to 11.1 months across the studies. PFS was reported in only two studies, with values of 7.5 months and 1.3 months. This systematic review highlights the evolving research on CAR-T cell therapy for GBM, emphasizing its potential despite challenges. Targeting antigens like EGFRvIII and IL13Rα2 shows promise in treating recurrent GBM. However, issues such as antigen escape, tumor heterogeneity, and immunosuppression require further optimization. Innovative delivery methods, combination therapies, and personalized approaches are crucial for enhancing CAR-T cell efficacy. Ongoing research is essential to refine these therapies and improve outcomes for GBM patients.

Purpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ² test was performed to assess the heterogeneity. Results: Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88–0.96), 83% (95% CI = 0.66–0.93), and 85% (95% CI = 0.71–0.93), and corresponding SPE values of 87% (95% CI = 0.82–0.90), 95% (95% CI = 0.90–0.98) and 90% (95% CI = 0.84–0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively). Conclusions: The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models.

Background and objective: Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms.Methods: A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test.Results: The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 – 0.91) and 87% (95% CI = 0.83 – 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%).Conclusion: The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.

Background: Brain calcifications, found in various conditions, may be incidental or crucial for diagnosis. They occur in physiological changes, infections, genetic diseases, neurodegenerative conditions, vascular syndromes, metabolic disorders, endocrine disorders, and primary tumors like oligodendroglioma. While often incidental, their presence can be vital for accurate diagnosis. Brain metastases are the most common neoplastic lesions in adults, with their incidence increasing due to improved diagnostic tools and overall oncologic patient survival. Calcifications within brain metastases are uncommon, mostly seen in patients treated with radiation therapy (RT). Although cases of calcified brain metastasis (CBM) are reported, large recent studies are scarce and the real incidence remains unclear. This lack of data raises the risk of underestimating CBM in the differential diagnosis of brain calcifications, potentially leading to misdiagnosis and delayed treatment, particularly when calcifications are observed without prior RT. Aim: This systematic review sought to assess the incidence of CBM in patients with identified primary tumors who underwent brain chemotherapy (CT) for staging. Additionally, the study aimed to explore the primary tumor types more frequently linked to CBM and determine whether CBM manifested initially or post-RT. Methods: A comprehensive search was performed across prominent medical databases (PubMed, Cochrane Library, and Embase) until 20 January 2024. The employed search method incorporated pertinent Medical Subject Headings (MeSH) and keywords such as “calcification”, “brain metastasis”, and “CT scan”. Studies included in this review were publications focusing on CBM in patients with identified primary tumors who underwent brain CT for staging. Results: In a systematic review of 39 studies on CBM in patients with identified primary tumors, 98 papers were initially identified, with 52 chosen for full-text analysis. Among them, 39 were deemed eligible after excluding 13 for various reasons. The study investigates brain calcifications in 1115 patients with metastatic disease, revealing that 7.89% had brain metastases, with 25% showing calcifications ab initio. These calcifications were more common than previously reported, emphasizing the need for attention to intraparenchymal brain calcifications in oncologic patients. Most CBM originated from lung and breast adenocarcinomas, and their correlation with primary tumor calcifications was inconclusive. Conclusions: The study highlights the significance of identifying evolving lesions in oncologic patients, calling for increased awareness among neuroradiologists and shedding light on the prevalence and characteristics of CBM.

Purpose: Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms. Methods: A systematic review of the literature and a meta-analysis were conducted, focusing on studies reporting on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test. Results: The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 – 0.91) and 87% (95% CI = 0.83 – 0.91), respectively. The best-performing features were the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (AUC 87% - 95%). Most studies applied a cross-validation analysis (92%). Conclusion: The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CTimaging modality is the best-performing, while the MRI CE-T1W is the most used.