OpenAI’s GPT-4V reliably identified the imaging modality and anatomic region but could not safely detect, classify, or rule out abnormalities on single MRI, CT, and radiographic images.

Background: Tractography based on diffusion-weighted magnetic resonance imaging (DWI) models the structural connectivity of the human brain. Deep brain stimulation (DBS) targeting the subthalamic nucleus is an effective treatment for advanced Parkinson's disease, but may induce adverse effects. This study investigated the relationship between structural connectivity patterns of DBS electrodes and stimulation-induced side effects. Materials and Methods: Twenty-one patients with Parkinson's disease treated with bilateral subthalamic DBS were examined. Overall, 168 electrode contacts were categorized as inducing or noninducing depending on their capability for inducing side effects such as motor effects, paresthesia, dysarthria, oculomotor effects, hyperkinesia, and other complications as assessed during the initial programming session. Furthermore, the connectivity of each contact with target regions was evaluated by probabilistic tractography based on DWI. Finally, stimulation sites and structural connectivity patterns of inducing and noninducing contacts were compared. Results: Inducing contacts differed across the various side effects and from those mitigating Parkinson's symptoms. Although contacts showed a largely overlapping spatial distribution within the subthalamic region, they could be distinguished by their connectivity patterns. In particular, inducing contacts were more likely connected with supplementary motor areas (hyperkinesia, dysarthria), frontal cortex (oculomotor), fibers of the internal capsule (paresthesia), and the basal ganglia-thalamo-cortical circuitry (dysarthria). Discussion: Side effects induced by DBS seem to be associated with distinct connectivity patterns. Cerebellar connections are hardly associated with side effects, although they seem relevant for mitigating motor symptoms in Parkinson's disease. A symptom-specific, connectivity-based approach for target planning in DBS may enhance treatment outcomes and reduce adverse effects. Impact statement: Tractography based on diffusion-weighted magnetic resonance imaging has become a prominent technique for investigating the connectivity of human brain networks in vivo . However, the relationship between structural connections and brain function is still hardly known. The present study examined the relationship between adverse behavioral effects induced by deep brain stimulation (DBS) and tractography patterns in individual brains. The results suggest that DBS-based side effects depend on the structural connections of electrode contacts rather than their location. Network-based target planning in DBS may improve treatment by avoiding side effects. Moreover, the adopted approach may serve as a paragon for investigating structure/function relationships.

Portable Computed Tomography (CT) scanners have revolutionized diagnostic imaging by bringing advanced imaging capabilities directly to the point of care in critical settings such as intensive care units, operating rooms, ambulances, and other resource-limited environments. Integrating portable CT into clinical workflows improves patient outcomes through immediate, on-site imaging, eliminating the risks and delays associated with intra-hospital transportation. These systems are indispensable in critical care, reducing complications and time to diagnosis while enabling timely interventions for life-threatening conditions such as intracranial hemorrhage. Portable CT scanners are also transforming prehospital care, particularly in mobile stroke units, where they facilitate rapid diagnosis and treatment of strokes. This review examines the fundamentals of portable CT technology and its transformative role in various clinical scenarios, such as ICUs and intraoperative imaging. It traces the evolution of CT from stationary energy integrating detector-based systems to portable photon counting detector-based systems, highlighting the advantages of this technological transition. Key benefits include reduced patient complications, enhanced spatial resolution, improved diagnostic accuracy, lower radiation doses, and superior material differentiation, illustrated through clinical examples. As innovation advances in detector design and artificial intelligence, portable CT scanners are expected to grow further, expanding their clinical applications and solidifying their role as essential tools in modern diagnostic radiology.

(1) Background: To determine the importance of diffusion-weighted whole-body MRI with background body signal suppression (DWIBS) in the staging process of patients with suspected head and neck carcinomas. (2) Methods: A total of 30 patients (24 male, 6 female) with a median age of 67 years with clinically suspected head and neck carcinoma with pathologic cervical nodal swelling in ultrasound underwent the staging procedure with computed tomography (CT) and whole-body MRI including DWIBS. (3) Results: In a total of 9 patients, abnormalities in the routine work-up of pretherapeutic staging were found. Five cases of either secondary cancer or distant metastases were only visible in DWIBS, while being missed on CT. One diagnosis was only detectable in CT and not in DWIBS, whereas three diagnoses were recognizable in both modalities. (4) Conclusions: DWIBS in addition to a standard neck MRI in cervical lymphadenopathy suspicious for head and neck cancer yielded additional clinically relevant diagnoses in 17% of cases that would have been missed by current staging routine procedures. DWIBS offered a negative predictive value of 98.78% for ruling out distant metastases or secondary malignancies.

Background: Growing research demonstrates the ability to predict histology or genetic information of various malignancies using radiomic features extracted from imaging data. This study aimed to investigate MRI-based radiomics in predicting the primary tumor of brain metastases through internal and external validation, using oversampling techniques to address the class imbalance. Methods: This IRB-approved retrospective multicenter study included brain metastases from lung cancer, melanoma, breast cancer, colorectal cancer, and a combined heterogenous group of other primary entities (5-class classification). Local data were acquired between 2003 and 2021 from 231 patients (545 metastases). External validation was performed with 82 patients (280 metastases) and 258 patients (809 metastases) from the publicly available Stanford BrainMetShare and the University of California San Francisco Brain Metastases Stereotactic Radiosurgery datasets, respectively. Preprocessing included brain extraction, bias correction, coregistration, intensity normalization, and semi-manual binary tumor segmentation. Two-thousand five hundred and twenty-eight radiomic features were extracted from T1w (± contrast), fluid-attenuated inversion recovery (FLAIR), and wavelet transforms for each sequence (8 decompositions). Random forest classifiers were trained with selected features on original and oversampled data (5-fold cross-validation) and evaluated on internal/external holdout test sets using accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). Results: Oversampling did not improve the overall unsatisfactory performance on the internal and external test sets. Incorrect data partitioning (oversampling before train/validation/test split) leads to a massive overestimation of model performance. Conclusions: Radiomics models’ capability to predict histologic or genomic data from imaging should be critically assessed; external validation is essential.