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.

Background/Objectives: This study aimed to determine if MRI features can distinguish between spinal infections caused by pyogenic bacteria and Mycobacterium tuberculosis. Methods: Patients underwent an MRI of the thoracolumbar spine with and without contrast. Three blinded observers assessed the images, using statistical tests for analysis. Results: Demographic characteristics and symptom duration were similar between patients with tuberculous and pyogenic spinal infections. In 36 cases of pyogenic infections, the MRI showed weakly delineated paravertebral tissue enhancement (76%), a hyperintense signal in the T2 TIRM sequences (89%), and homogeneous vertebral body enhancement (89%). In 32 cases of Mycobacterium infections, the MRI revealed well-delineated paravertebral changes, mixed vertebral body signals, and variable enhancement. Pyogenic infections were more often found in the lumbar spine (67%) and typically involved two vertebrae, while tuberculous infections preferred the thoracic spine (75%) and often involved two vertebrae, with 25% affecting three or more vertebrae. Conclusions: The MRI features can help differentiate between pyogenic and tuberculous spine infections, though none are definitive. The study suggests that MRI can be used for initial differentiation or as a diagnostic tool when biopsy or surgical exploration is not possible.

Purpose: Artificial intelligence is increasingly developed and deployed to support radiological image interpretation, triage, segmentation and workflow prioritisation. However, high performance reported in retrospective studies does not necessarily translate into safe and useful deployment in routine practice. This narrative review examines why this translational gap persists and what radiology departments, researchers and developers should address before clinical implementation. Methods: We synthesised methodological, empirical and regulatory literature on the development, validation, deployment and governance of radiology AI. Radiology was used as the primary setting, with adjacent imaging fields discussed only where they illustrate broader mechanisms relevant to medical image analysis. Results: Recurrent barriers include limited data quality and representativeness, scanner- and protocol-related domain shift, shortcut learning, hidden stratification, insufficient external and prospective validation, poor calibration, over-reliance on global metrics and weak workflow integration. Model performance can be shaped by scanner protocols, reconstruction methods, local reporting practices, patient selection and institutional workflows beyond the underlying pathology. In Europe, GDPR-based data governance, the Medical Device Regulation, the AI Act and the European Health Data Space further shape clinical deployment. Conclusions: Emerging approaches, including foundation models, generative AI, multimodal systems, federated learning and local adaptation, may address selected technical constraints, but they introduce new uncertainties and do not replace independent validation in the intended clinical setting. Radiology AI should be evaluated not only as an algorithmic model, but as a clinical decision-support component embedded in radiology practice, requiring external and preferably prospective validation, subgroup analysis, calibration assessment, human oversight, workflow integration and post-deployment monitoring.