The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Alterations in nutritional status, in particular sarcopenia, have been extensively associated with a poor prognosis in cirrhotic patients regardless of the etiology of liver disease. Less is known about the predictive value of myosteatosis, defined as pathological fat infiltration into the skeletal muscle. We retrospectively analyzed a cohort of 151 cirrhotic patients with unresectable hepatocellular carcinoma (HCC) who underwent their first trans-arterial embolization (TAE) between 1 March 2011 and 1 July 2019 at our Institution. Clinical and biochemical data were collected. Sarcopenia was assessed using the L3-SMI method while myosteatosis with a dedicated segmentation suite (3D Slicer), using a single slice at an axial plane located at L3 and calculating the IMAC (Intramuscular Adipose Tissue Content Index). The sex-specific cut-off values for defining myosteatosis were IMAC > −0.44 in males and >−0.31 in females. In our cohort, 115 (76%) patients were included in the myosteatosis group; 128 (85%) patients had a coexistent diagnosis of sarcopenia. Patients with myosteatosis were significantly older and showed higher BMI than patients without myosteatosis. In addition, male gender and alcoholic- or metabolic-related cirrhosis were most represented in the myosteatosis group. Myosteatosis was not associated with a different HCC burden, length of hospitalization, complication rate, and readmission in the first 30 days after discharge. Overall survival was not influenced by the presence of myosteatosis.

Background: Emergency laparotomy (EL) is related to a high risk of morbidity and mortality. Sarcopenia (low skeletal muscle mass) and myosteatosis (poor muscle quality) have emerged as prognostic indicators in various clinical contexts. This study evaluated the impact of these conditions on postoperative outcomes in patients undergoing EL for abdominal emergencies. Methods: A retrospective analysis was conducted on 242 patients who underwent EL between January 2016 and December 2023. Skeletal muscle index (SMI) and muscle radiation attenuation (MRA) were measured using CT imaging at the L3 level. Sarcopenia was defined as SMI ≤ 41.6 cm²/m² for men and ≤ 32 cm²/m² for women. Myosteatosis was defined as MRA ≤ 29.3 HU for men and ≤ 22 HU for women. Outcomes included 30-day mortality, hospital length of stay (h-LOS), severe complications (Clavien-Dindo ≥ 3), and Intensive Care Unit (ICU) admission. Results: Of the 242 patients (median age: 70; 51.2% men), 42.6% were sarcopenic and 78.1% had myosteatosis. Sarcopenia was not significantly associated with any postoperative outcomes. Conversely, myosteatosis was significantly associated with longer h-LOS (17 vs. 8 days; p < 0.001), higher rates of severe complications (37.1% vs. 22.7%; p = 0.048), and ICU admission (48.2% vs. 28.3%; p = 0.010), but not with 30-day mortality. Multivariate analysis confirmed myosteatosis as an independent predictor of prolonged hospital stay (HR 0.59, 95% CI: 0.42–0.84 p = 0.003). Conclusions: Myosteatosis, rather than sarcopenia, is associated with worse postoperative outcomes following EL for abdominal emergencies. Including myosteatosis in preoperative risk assessments may improve the identification of high-risk patients and guide perioperative management.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.