Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

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.

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures.

(1) Background: The value of dual-energy CT angiography (DE-CTA) in the detection of peripheral arterial disease (PAD) has been widely recognized. We aim to evaluate the diagnostic accuracy of virtual non-contrast (VNC) imaging of DE-CTA compared to true non-contrast phase (TNC). (2) Methods: Our Internal Review Board (IRB) approved prospective study enrolled 40 patients (28 men, 12 women; median age 69 y, range 41–93 y) who underwent lower extremity DE-CTA for symptomatic PAD. Mean attenuation values of TNC and VNC were obtained by placing circular regions of interest (ROI) at five levels from the aortic to the popliteal arterial lumen, reported in Hounsfield units (HU), and compared using a two-sample t-test. The subjective quality of VNC images was assessed by two independent radiologists with 10 and 4 years of CTA-imaging experience according to a 4-point scale and verified by the intra-class correlation coefficient (ICC). Dose Length Product (DLP) values of each DE-CTA examination were also considered. (3) Results: Except for the external iliac artery, VNC attenuation values were significantly lower than the corresponding TNC values at all levels, with a mean difference ranging from 14.1 and 8.7 HU. At qualitative analysis, VNC images were considered excellent to diagnose in 40%, good in 50%, and sufficient in 10% of cases. No cases of non-diagnostic VNC imaging were reported. Avoiding the TNC phase, a mean reduction in DLP of 54% for each DE-CTA was estimated. (4) Conclusions: TNC and VNC images showed comparable reliability and diagnostic accuracy in the detection of PAD. VNC may be considered a promising substitute for TNC from the perspectives of dose reduction and workflow optimization.

Background: Imaging findings have a prominent role in early and correct identification of ovarian dysgerminoma, the most common ovarian malignant germ cell tumor (OMGCT). Despite Computed Tomography (CT) is widely used, Magnetic Resonance Imaging (MRI) has proved to be superior in adnexal masses characterization. Limited data and small series are available concerning MRI aspects of dysgerminoma. Case presentation: From January 2012 to December 2018, a database of solid ovarian masses was retrospectively reviewed. Eight patients with histologically proven pure ovarian dysgerminoma and complete imaging available were identified and analyzed. Imaging findings were evaluated separately by two radiologists expert in female genito-urinary MRI. Conclusions: MRI findings of a lobulated, purely solid, encapsulated mass with hyper-intensity of lobules and hypo-intensity of septa on T2w images contribute to differentiate dysgerminomas from other ovarian neoplasms.

(1) Background: Type 2 diabetes is a major cause of incidences and the progression of peripheral artery disease (PAD). Bone marrow edema (BME) is an important finding suggestive of underlying bone inflammation in non-traumatic diabetic patients with PAD. Our aim was to evaluate the presence, severity, and clinical implications of BME detected by virtual non-calcium application (VNCa) of dual-energy CT angiography (DE-CTA). (2) Methods: A consecutive series of 76 diabetic patients (55 men; mean age 71.6 ± 11.2 yrs) submitted to lower limb DE-CTA for PAD evaluation and revascularization planning, which were retrospectively analyzed. VNCa images were independently and blindly revised for the presence, location, and severity of BME by two radiologists with 10 years of experience. BME and non-BME groups were evaluated in terms of PAD clinical severity and 6-month secondary major amputation rate. (3) Results: BME was present in 17 (22%) cases, while 59 (78%) patients were non-BME. The BME group showed a significantly higher incidence of major amputation (p < 0.001) and a significantly higher number of patients with advanced clinical stages of PAD compared to the non-BME group (p = 0.024). (4) Conclusions: Lower limb DE-CTA with VNCa application is a useful tool in the detection of BME in diabetic patients with PAD, simultaneously enabling the evaluation of the severity and location of the arterial disease for revascularization planning. BME presence could be a marker of clinically severe PAD and a possible risk factor for revascularization failure.