Gastric cancer (GC) represents the fifth most frequently diagnosed cancer worldwide, with a poor prognosis in patients with advanced disease despite many improvements in systemic treatments in the last decade. In fact, GC has shown resistance to several treatment options, and thus, notable efforts have been focused on the research and identification of novel therapeutic targets in this setting. The tumor microenvironment (TME) has emerged as a potential therapeutic target in several malignancies including GC, due to its pivotal role in cancer progression and drug resistance. Therefore, several agents and therapeutic strategies targeting the TME are currently under assessment in both preclinical and clinical studies. The present study provides an overview of available evidence of the inflammatory TME in GC, highlighting different types of tumor-associated cells and implications for future therapeutic strategies.

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

Background: To evaluate the segmental distribution of hepatocellular carcinoma (HCC) according to Couinaud’s anatomical division in cirrhotic patients. Methods: Between 2020 and 2021, a total of 322 HCC nodules were diagnosed in 217 cirrhotic patients who underwent computed tomography (CT) or magnetic resonance imaging (MRI) for the evaluation of suspicious nodules (>1 cm) detected during ultrasound surveillance. For each patient, the segmental position of the HCC nodule was recorded according to Couinaud’s description. The clinical data and nodule characteristics were collected. Results: A total of 234 (72.7%) HCC nodules were situated in the right lobe whereas 79 (24.5%) were detected in the left lobe (p < 0.0001) and only 9 nodules were in the caudate lobe (2.8%). HCC was most common in segment 8 (n = 88, 27.4%) and least common in segment 1 (n = 9, 2.8%). No significant differences were found in the frequencies of segmental or lobar involvement considering patient demographic and clinical characteristics, nodule dimension, or disease appearance. Conclusions: The intrahepatic distribution of HCC differs among Couinaud’s segments, with segment 8 being the most common location and segment 1 being the least common. The segmental distribution of tumour location was similar to the normal liver volume distribution, supporting a possible correlation between HCC location and the volume of hepatic segments and/or the volumetric distribution of the portal blood flow.

The prevalence of primary hypertension in pediatric patients is increasing, especially as a result of the increased prevalence of obesity in children. New diagnostic guidelines for blood pressure were published by the American Academy of Pediatrics (AAP) in 2017 to better define classes of hypertension in children. The aim of our study is to evaluate the impact of new guidelines on diagnosis of hypertension in pediatrics and their capacity to identify the presence of cardiovascular and metabolic risk. Methods: Retrospective clinical and laboratory data from 489 overweight and obese children and adolescents were reviewed. Children were classified according to the 2004 and 2017 AAP guidelines for systolic and diastolic blood pressure. Lipid profile and glucose metabolism data were recorded; triglyceride/HDL ratio (TG/HDL) was calculated as an index of endothelial dysfunction. Hepatic steatosis was detected using the ultrasonographic steatosis score. Results: Children with elevated blood pressure increased from 12.5% with the 2004 AAP to 23.1% with the 2017 AAP criteria (p < 0.001). There was a statistically significant increase in children with high blood pressure in all age groups according to the new cut-off values. Notably, the diagnosis of hypertension according to 2017 AAP criteria had a greater positive association with Hepatic Steatosis (rho 0.2, p < 0.001) and TG/HDL ratio (rho 0.125, p = 0.025). Conclusions: The 2017 AAP tables offer the opportunity to better identify overweight and obese children at risk for organ damage, allowing an earlier and more impactful prevention strategy to be designed.