Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.

Background: Autoimmune hypophysitis is a rare disease with a natural progression that is not well known. Aim: To collect representative data on clinical features of autoimmune hypophysitis and better characterize the disease. Patients and Methods: A prospective single-center study was designed. Autoimmune hypophysitis-affected patients evaluated from 2011 at our tertiary care Pituitary Unit were enrolled. After ruling out other pituitary masses and secondary causes of hypophysitis, autoimmune hypophysitis was the diagnosis of exclusion. Autoimmune hypophysitis was classified as adenohypophysitis, panhypophysitis, and infundibuloneurohypophysitis according to clinical and neuroradiological findings. Results: A total of 21 patients met the inclusion criteria: 9 were diagnosed with adenohypophysitis, 4 with panhypophysitis, and 8 with infundibuloneurohypophysitis. The frequency of secondary hypoadrenalism was similar in adenohypophysitis, panhypophysitis, and infundibuloneurohypophysitis. Growth hormone deficit and secondary hypogonadism occurred more frequently in infundibuloneurohypophysitis than in adenohypophysitis and panhypophysitis (p = 0.009; p = 0.04). All cases of multiple pituitary secretion deficits occurred in cases of infundibuloneurohypophysitis (p = 0.04). No correlations between hypophysitis subtype and anti-pituitary and anti-hypothalamus autoantibodies were found. A higher frequency of extractable nuclear antigens (ENA) and anti-nuclear antibodies (ANA) was found in cases of panhypophysitis (OR 5.0, 95% CI 0.86-28.8, p < 0.001, and OR 1.8, 95% CI 1.1-3.2, p = 0.02, respectively) as compared to adenohypophysitis and infundibuloneurohypophysitis. Conclusion: Infundibuloneurohypophysitis should be taken into account in the etiological diagnosis of hypopituitarism, particularly if it is associated with diabetes insipidus and in cases of growth hormone deficit, secondary hypogonadism, or multiple hormone deficits. Contrast-enhanced MRI is crucial for the clinical and noninvasive diagnosis of hypophysitis. Screening for autoantibodies, particularly anti-ENA and anti-ANA, is strongly suggested in the clinical context of hypophysitis.

BACKGROUND AND PURPOSE: A recent study using task-based fMRI demonstrated that the middle frontal gyrus is comparable with Broca9s area in its ability to determine language laterality using a measure of verbal fluency. This study investigated whether the middle frontal gyrus can be used as an indicator for language-hemispheric dominance in patients with brain tumors using task-free resting-state fMRI. We hypothesized that no significant difference in language lateralization would occur between the middle frontal gyrus and Broca area and that the middle frontal gyrus can serve as a simple and reliable means of measuring language laterality. MATERIALS AND METHODS: Using resting-state fMRI, we compared the middle frontal gyrus with the Broca area in 51 patients with glial neoplasms for voxel activation, the language laterality index, and the effect of tumor grade on the laterality index. The laterality index derived by resting-state fMRI and task-based fMRI was compared in a subset of 40 patients. RESULTS: Voxel activations in the left middle frontal gyrus and left Broca area were positively correlated (r = 0.47, P < .001). Positive correlations were seen between the laterality index of the Broca area and middle frontal gyrus regions (r = 0.56, P < .0005). Twenty-seven of 40 patients (67.5%) showed concordance of the laterality index based on the Broca area using resting-state fMRI and the laterality index based on a language task. Thirty of 40 patients (75%) showed concordance of the laterality index based on the middle frontal gyrus using resting-state fMRI and the laterality index based on a language task. CONCLUSIONS: The middle frontal gyrus is comparable with the Broca area in its ability to determine hemispheric dominance for language using resting-state fMRI. Our results suggest the addition of resting-state fMRI of the middle frontal gyrus to the list of noninvasive modalities that could be used in patients with gliomas to evaluate hemispheric dominance of language before tumor resection. In patients who cannot participate in traditional task-based fMRI, resting-state fMRI offers a task-free alternate to presurgically map the eloquent cortex.

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.