Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may ...still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and ...machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
Tuberous sclerosis complex (TSC) is an autosomal dominant condition clinically presenting with heterogenous clinical features. Multiple neuroradiological manifestations have been associated with TSC, ...such as tubers, radial migration lines, subependymal nodules, subependymal giant cell astrocytomas, and cyst-like lesions of the white matter (CLLWMs). The latter have been described as non-enhancing well-defined cysts whose pathogenesis is still unknown. We describe 2 TSC patients with CLLWM showing contrast enhancement after Gadolinium injection, a previously unreported entity.
Purpose
Cerebellar ataxias are a large and heterogeneous group of disorders. The evaluation of brain parenchyma via MRI plays a central role in the diagnostic assessment of these conditions, being ...mandatory to exclude the presence of other underlying causes in determining the clinical phenotype. Once these possible causes are ruled out, the diagnosis is usually researched in the wide range of hereditary or sporadic ataxias.
Methods
We here propose a review of the main clinical and conventional imaging findings of the most common hereditary degenerative ataxias, to help neuroradiologists in the evaluation of these patients.
Results
Hereditary degenerative ataxias are all usually characterized from a neuroimaging standpoint by the presence, in almost all cases, of cerebellar atrophy. Nevertheless, a proper assessment of imaging data, extending beyond the mere evaluation of cerebellar atrophy, evaluating also the pattern of volume loss as well as concomitant MRI signs, is crucial to achieve a proper diagnosis.
Conclusion
The integration of typical neuroradiological characteristics, along with patient’s clinical history and laboratory data, could allow the neuroradiologist to identify some conditions and exclude others, addressing the neurologist to the more appropriate genetic testing.
Background
Cholesteatoma is caused by disorders of the middle ear ventilation that trigger a progressive series of events responsible for its formation. The aim of this study was to identify possible ...radiological CT-derived parameters predisposing to ventilation disorders and cholesteatoma.
Methods
In this retrospective study, patients diagnosed with cholesteatomatous chronic otitis media who underwent temporal bone CT and open tympanoplasty surgery have been included, as well as control patients with clinical examination negative for organic otological pathology who underwent temporal bone CT for other reasons. For each patient, the following parameters have been extracted from CT volumes: degree of mastoid pneumatization, prominence of the cog, patency of the Eustachian tube, antrum width, aditus width, anterior and posterior epitympanic widths, and epitympanic height.
Results
Sixty patients have been included, thirty of whom belonged to the group of patients with cholesteatoma and the remaining part to the group of patients without organic otological pathology. The prevalence of a low degree of mastoid pneumatization was significantly higher among patients with cholesteatoma, as well as for the prevalence of cog prominence (
p
< 0.001). All the continuous variables were found to have statistical significance (
p
< 0.05) in the comparison between groups except for the width of the antrum.
Conclusion
Mastoid pneumatization degree, prominence of the cog and epitympanic measures based on temporal bone CT could be good radiological correlates of the ventilatory capabilities of the epitympanum which, if compromised, can facilitate the development of cholesteatoma.
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have ...been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles ...are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.
Background and aim
Neurodegeneration with brain iron accumulation (NBIA) and Wilson’s disease (WD) is considered the prototype of neurodegenerative disorders characterised by the overloading of iron ...and copper in the central nervous system. Growing evidence has unveiled the involvement of these metals in brain cortical neurotransmission. Aim of this study was to assess cortical excitability profile due to copper and iron overload.
Methods
Three patients affected by NBIA, namely two patients with a recessive hereditary parkinsonism (PARK9) and one patient with aceruloplasminemia and 7 patients with neurological WD underwent transcranial magnetic stimulation (TMS) protocols to assess cortical excitability. Specifically, we evaluated the motor thresholds that reflect membrane excitability related to the voltage-gated sodium channels in the neurons of the motor system and the ease of activation of motor cortex via glutamatergic networks, and ad hoc TMS protocols to probe inhibitory-GABAergic (short interval intracortical inhibition, SICI; short-latency afferent inhibition, SAI; cortical silent period, CSP) and excitatory intracortical circuitry (intracortical facilitation, ICF).
Results
Patients with NBIA exhibited an abnormal prolongation of CSP respect to HC and WD patients. On the contrary, neurological WD displayed higher motor thresholds and reduced CSP and SICI.
Conclusion
Hereditary conditions due to overload of copper and iron exhibited peculiar cortical excitability profiles that can help during differential diagnosis between these conditions. Moreover, such results can give us more clues about the role of metals in acquired neurodegenerative disorders, such as Parkinson disease, Alzheimer disease, and multiple sclerosis.
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