Correlation Analysis on Anatomical Variants of Accessory Foramina in the Sphenoid Bone for Oncological Surgery

The sphenoid bone presents several anatomical variations, including accessory foramina, such as the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal, which may be involved in tumor invasion or surgery of surrounding structures. Therefore, clinicians and surgeons have to consider these variants when planning surgical interventions of the cranial base. The prevalence of each variant is reported in the published literature, but very little information is available on the possible correlation among different variants. Here, 300 CT scans of patients (equally divided among males and females) were retrospectively assessed to investigate the presence of the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal. Possible differences in the prevalence of each accessory foramen according to sex were assessed, as well as possible correlations among different variants through the Chi-square test (p p > 0.01). A significant positive correlation was found between the foramen of Vesalius and canaliculus innominatus, both in males and in females (p < 0.01). In detail, subjects with canaliculus innominatus in 85.7–100.0% of cases also showed the foramen of Vesalius, independently from sex and side. The present study provided novel data about the prevalence of four accessory foramina of the sphenoid bone in an Italian population, and a correlation between the foramen of Vesalius and the canaliculus innominatus was found for the first time. As these accessory foramina host neurovascular structures, the results of this study are thus useful for appropriate planning surgical procedures that are tailored to the anatomical configuration of the patient and for improving techniques to avoid accidental injuries in cranial base surgery. Knowledge of the topography, frequencies and the presence/absence of these additional foramina are pivotal for a successful procedure. Clinicians and surgeons may benefit from these novel data for appropriate recognition of the variants, decision-making, pre-operative and treatment planning, improvement of the procedures, screening of patients and prevention of misdiagnosis

Exploring the Role of ChatGPT in Oncology: Providing Information and Support for Cancer Patients

Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients’ emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies

Computed Tomography Urography: State of the Art and Beyond

Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients’ outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique

Portable Dynamic Chest Radiography: Literature Review and Potential Bedside Applications

Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient’s bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient’s bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method

Anatomy of the Mental Foramen: Relationship among Different Metrical Parameters for Accurate Localization

Purpose: The mental foramen (MF) is important in dental surgery procedures for preventing possible iatrogenic lesions and for anesthetic procedures. This paper aims at expanding knowledge on the metrical characteristics that are useful for the correct anatomical assessment of the MF. Materials and Methods: On 100 CBCT scans (50 males and 50 females), height, width, depth, linear distance from the inferior and superior mandibular edge and position according to teeth and dental apices were analyzed. Differences according to sex and the side for each metrical measurement and for teeth and dental apices were assessed through a two-way ANOVA test and Chi-square test, respectively. Pearson’s test and a one-way ANOVA test were used to test correlations among the chosen parameters (p p p < 0.01). Conclusions: Novel data about the anatomical position of the mental foramen are described, which are useful for the management of surgical procedures

Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients&rsquo; outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients&rsquo; lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS&ndash;PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients&rsquo; clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease&rsquo;s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field