Background: The aim of this multicenter study was to evaluate the prevalence and features of dual left anterior descending artery (LAD) subtypes using coronary CT angiography (CCTA). Methods: A retrospective multicenter analysis of 2083 CCTA from December 2020 to November 2022 was conducted to search for the presence and morphological features of dual LAD. The two classifications used were the updated classification of Spindola-Franco and the Jariwala classification. Statistical tests were conducted to evaluate the prevalence of dual LADs among sexes and its association with angina in patients without significant coronary stenoses and/or associated cardiac anomalies. Results: Dual LAD was observed in 124 (5.96%) patients analyzed. According to the Spindola-Franco revisited classification, type I dual LAD was the most common (71/124, 57.26%). According to the Jariwala classification, all cases were group I. In the general population, there was a higher prevalence of dual LAD among females (7.3% females vs. 5.1% males; p value: 0.04). No statistically significant difference was found in the prevalence of angina in the dual LAD population compared to the no dual LAD population (2.1% vs. 1.5%; p value: 0.10). Conclusions: The acknowledgment and reporting of LAD duplication is helpful for an optimal management of coronary patients with this condition. Dual LAD was more frequent in the female population, mainly not related with angina. Myocardial bridge was more frequent in the dual LAD population than in the no dual LAD population.

Prostate cancer is one of the most common tumors among the male population. Magnetic resonance imaging (MRI), standardized by the PI-RADS version 2.1 scoring system, has a fundamental role in detecting prostate cancer and evaluating its aggressiveness. Diffusion-weighted imaging sequences and apparent diffusion coefficient values, in particular, are considered fundamental for the detection and characterization of lesions. In 2016 the International Society of Urological Pathology introduced a new anatomopathological 5-grade scoring system for prostate cancer. The aim of this study is to evaluate the correlation between quantitative apparent diffusion coefficient values (ADC) derived from diffusion-weighted imaging (DWI) sequences and the International Society of Urological Pathology (ISUP) and PI-RADS groups. Our retrospective study included 143 patients with 154 suspicious lesions, observed on prostate magnetic resonance imaging and compared with the histological results of the biopsy. We observed that ADC values can aid in discriminating between not clinically significant (ISUP 1) and clinically significant (ISUP 2-5) prostate cancers. In fact, ADC values were lower in ISUP 5 lesions than in negative lesions. We also found a correlation between ADC values and PI-RADS groups; we noted lower ADC values in the PI-RADS 5 and PI-RADS 4 groups than in the PI-RADS 3 group. In conclusion, quantitative apparent diffusion coefficient values can be useful to assess the aggressiveness of prostate cancer.

This study aimed to assess the accuracy of the Node-RADS scoring system in assessing metastatic lymph node (LN) involvement in patients with head and neck squamous cell carcinoma (HNSCC), a critical factor for treatment planning and prognosis. A retrospective analysis was conducted on 42 HNSCC patients who underwent preoperative MRI and lymph node dissection. Two radiologists independently evaluated the MRI scans using the Node-RADS system, and the results were compared with postoperative histopathological findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of Node-RADS were calculated, considering LNs with Node-RADS scores of 4 and 5 as positive. LN features such as size, texture, necrosis, border irregularity, and shape were analyzed for their correlation with metastatic involvement. Irregular/ill-defined borders were also evaluated for the detection of Extranodal extension (ENE+). Out of 118 LNs assessed, 26 (22.03 %) were metastatic (N+), and 8 (7.78 %) were ENE+. Node-RADS demonstrated a sensitivity of 61.54 %, specificity of 89.69 %, PPV of 76.19 %, NPV of 89.69 %, and an overall accuracy of 87.29 %. Significant correlations were observed between LN characteristics like necrosis, border irregularity, shape, and histopathological results (p < 0.0001). The Node-RADS system exhibited high specificity and accuracy in identifying metastatic LNs in HNSCC patients, making it a promising tool for standardizing preoperative imaging assessments. However, further studies are needed to validate its application and improve its integration with advanced imaging techniques for enhanced diagnostic precision.

Objectives: This study aimed to evaluate the diagnostic performance of various MRI sequences in detecting nodal metastasis (N+) and extranodal extension (ENE+) in patients with head and neck squamous cell carcinoma (HNSCC). Methods: A retrospective analysis was conducted on 42 patients with HNSCC who underwent preoperative MRI and subsequent surgical lymph node dissection between June 2021 and December 2023. Lymph node MRI features were evaluated on five different MRI sequences. For each rN+ case, the presence of radiological extranodal extension (rENE+) was assessed independently in every MRI sequence by analyzing three characteristics. ENE was deemed positive if at least one of three criteria considered was present. Results: All of the MRI sequences demonstrated slightly high accuracy (~76%) for detecting N+, with T1WI, STIR, and CE THRIVE showing comparable sensitivities (60–65%). The STIR sequence exhibited the highest sensitivity (75%) and nearly the highest accuracy (91%) for detecting ENE+. Capsular irregularity and necrosis showed high specificity across sequences, while the loss of fatty hilum and nodal size had lower performance. Conclusions: Tailoring MRI protocols to leverage the strengths of specific sequences can significantly improve the diagnostic accuracy, aiding in better patient management and treatment outcomes.

Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.