The blood–brain barrier (BBB) poses a significant challenge to drug delivery for brain tumors, with most chemotherapeutics having limited permeability into non-malignant brain tissue and only restricted access to primary and metastatic brain cancers. Consequently, due to the drug’s inability to effectively penetrate the BBB, outcomes following brain chemotherapy continue to be suboptimal. Several methods to open the BBB and obtain higher drug concentrations in tumors have been proposed, with the selection of the optimal method depending on the size of the targeted tumor volume, the chosen therapeutic agent, and individual patient characteristics. Herein, we aim to comprehensively describe osmotic disruption with intra-arterial drug administration, intrathecal/intraventricular administration, laser interstitial thermal therapy, convection-enhanced delivery, and ultrasound methods, including high-intensity focused and low-intensity ultrasound as well as tumor-treating fields. We explain the scientific concept behind each method, preclinical/clinical research, advantages and disadvantages, indications, and potential avenues for improvement. Given that each method has its limitations, it is unlikely that the future of BBB disruption will rely on a single method but rather on a synergistic effect of a combined approach. Disruption of the BBB with osmotic infusion or high-intensity focused ultrasound, followed by the intra-arterial delivery of drugs, is a promising approach. Real-time monitoring of drug delivery will be necessary for optimal results.

As a significant cause of intracerebral hemorrhages, seizures, and neurological decline, brain arteriovenous malformations (bAVMs) are a rare group of complex vascular lesions with devastating implications for patients’ quality of life. Although the concerted effort of the scientific community has improved our understanding of bAVM biology, the exact mechanism continues to be elucidated. Furthermore, to this day, due to the high heterogeneity of bAVMs as well as the lack of objective data brought by the lack of evaluative and comparative studies, there is no clear consensus on the treatment of this life-threatening and dynamic disease. As a consequence, patients often fall short of obtaining the optimal treatment. Endovascular embolization is an inherent part of multidisciplinary bAVM management that can be used in various clinical scenarios, each with different objectives. Well-trained neuro-interventional centers are proficient at curing bAVMs that are smaller than 3 cm; are located superficially in noneloquent areas; and have fewer, larger, and less tortuous feeding arteries. The transvenous approach is an emerging effective and safe technique that potentially offers a chance to cure previously untreatable bAVMs. This review provides the state of the art in all aspects of endovascular embolization in the management of bAVMs.

This study examines corpus callosum pathology in children with spastic cerebral palsy aged 7 to 15 years and to investigates the relation between corpus callosum areas and clinical picture. Magnetic resonance images of 46 patients were reviewed prospectively. Twenty-two patients with cerebral palsy were age and gender matched with the control patients. The cerebral palsy group had a significantly smaller mean corpus callosum surface area than did the control group. The cerebral palsy group also had a significantly smaller mean internal skull surface area measurement than did the control group. The corpus callosum/internal skull surface area ratio was also smaller for those with cerebral palsy. Wechsler Intelligence Scale Verbal IQ scores were associated with the surface area of the corpus callosum in cerebral palsy patients. A significant relationship between corpus callosum surface area and IQ scores in children with cerebral palsy was found. A positive correlation between internal skull surface area and IQ scores in children with cerebral palsy was noted. A significant correlation between Apgar score and corpus callosum surface area in the cerebral palsy group was found. A negative correlation between corpus callosum surface area and the Gross Motor Function Classification System in patients with cerebral palsy was noted.

Radiomics is a process of extracting many quantitative data obtained from medical images and analysing them. In neuroradiology it may be used to discover magnetic resonance imaging (MRI) features of glioblastomas that are impossible to identify by human vision alone. In this article, the authors describe the methodology and their first experience in creating a predictive model based on radiomic features obtained from the preoperative MRI examination of patients with glioblastoma. Early identification of malignant glioblastoma subtypes characterized by different prognoses and responses to treatment would greatly facilitate the implementation of targeted therapy, which appears to be the future of glioblastoma treatment.

Lower-limb alignment deformities constitute a significant clinical concern, as they can lead to serious complications, including progressive degenerative diseases and disabilities. Rotational deformities may give rise to conditions such as joint arthrosis, patellar instability, and the degeneration of the patellofemoral cartilage. Therefore, a comprehensive evaluation of lower-limb alignment is essential for the effective patient management, preoperative planning, and successful correction of these deformities. The primary assessment method employs full-length standing radiographs in the anteroposterior (AP) projection, which facilitates accurate measurements of the anatomical and mechanical axes of the lower limb, including angles and deviations. The outcomes of this analysis are vital for the meticulous planning of osteotomy and total knee arthroplasty (TKA). In addition, computed tomography (CT) provides a specialized approach for the precise evaluation of femoral and tibial rotation. In this area, there are potential opportunities for the implementation of AI-based automated measurement systems.

Background/Objectives: High-grade gliomas are the most common primary malignant brain tumors in adults. These neoplasms remain predominantly incurable due to the genetic diversity within each tumor, leading to varied responses to specific drug therapies. With the advent of new targeted and immune therapies, which have demonstrated promising outcomes in clinical trials, there is a growing need for image-based techniques to enable early prediction of treatment response. This study aimed to evaluate the potential of radiomics and artificial intelligence implementation in predicting progression-free survival (PFS) in patients with highest-grade glioma (CNS WHO 4) undergoing a standard treatment plan. Methods: In this retrospective study, prediction models were developed in a cohort of 51 patients with pathologically confirmed highest-grade glioma (CNS WHO 4) from the authors’ institution and the repository of the Cancer Imaging Archive (TCIA). Only patients with confirmed recurrence after complete tumor resection with adjuvant radiotherapy and chemotherapy with temozolomide were included. For each patient, 109 radiomic features of the tumor were obtained from a preoperative magnetic resonance imaging (MRI) examination. Four clinical features were added manually—sex, weight, age at the time of diagnosis, and the lobe of the brain where the tumor was located. The data label was the time to recurrence, which was determined based on follow-up MRI scans. Artificial intelligence algorithms were built to predict PFS in the training set (n = 75%) and then validate it in the test set (n = 25%). The performance of each model in both the training and test datasets was assessed using mean absolute percentage error (MAPE). Results: In the test set, the random forest model showed the highest predictive performance with 1-MAPE = 92.27% and a C-index of 0.9544. The decision tree, gradient booster, and artificial neural network models showed slightly lower effectiveness with 1-MAPE of 88.31%, 80.21%, and 91.29%, respectively. Conclusions: Four of the six models built gave satisfactory results. These results show that artificial intelligence models combined with radiomic features could be useful for predicting the progression-free survival of high-grade glioma patients. This could be beneficial for risk stratification of patients, enhancing the potential for personalized treatment plans and improving overall survival. Further investigation is necessary with an expanded sample size and external multicenter validation.