Introduction: Evaluation of pancreatic ductal adenocarcinoma (PDAC) after chemoradiotherapy downstaging is challenging due to computed tomography (CT) overestimation of tumor extension and residual vascular involvement, limiting access to surgery to some patients with potentially resectable tumors. With this study, we wanted to assess which radiological findings are most reliable at pre-operative imaging in the evaluation of PDAC after chemoradiotherapy in order to achieve complete resection. Methods: We retrospectively enrolled 71 patients with locally advanced and borderline resectable PDAC who underwent neoadjuvant chemoradiotherapy. Pre-operative CT or magnetic resonance (MR) have been evaluated by three radiologists to assess major qualitative and quantitative parameters of lesions. Accuracy, sensitivity, and specificity compared to anatomopathological results were evaluated for each parameter. Cohen’s K-coefficient has been calculated to evaluate the inter-observer agreement (IOA). Both single and consensus lecture have been tested. Different dimensional cut-offs were tested to categorize tumors according to their major axis and to compare with anatomopathological diameter, tumor persistence, and margin infiltration. Results: A 25 mm cut-off was 67% sensitive, 90% specific, and 77% accurate in assessing real tumor dimension. 25 mm cut-off reported a 64% sensitivity, 78% specificity, and 69% accuracy in assessing R0 resection. Each 5 mm increment of major axis dimension there is an odds ratio (OR) 1.79 (95% CI 1.13–2.80, p = 0.012) for R+ resection. Imaging presence of the perivascular cuff is not associated with tumor persistence and resection margin infiltration (p = 0.362). Lesion enhancement and pattern homogeneity were not accurate in determining tumor persistence. IOA was generally poor to fair, except for >25 mm cut-off classification where IOA was moderate. Diagnostic accuracy is superior in consensus lecture rather than single lecture. Conclusion: Imaging methods tend to underestimate PDAC resectability after neoadjuvant-CRT. IOA is poor to fair in evaluating most of the qualitative parameters of downstaged pancreatic adenocarcinoma. Surgery should be considered for downstaged borderline resectable PDACs, independently from perivascular cuff presence, especially for tumors smaller than 25 mm.

Background: Acute myocardial infarction (AMI) survivors are at risk of major adverse cardiac events and their risk stratification is a prerequisite to tailored therapeutic approaches. Biomarkers could be of great utility in this setting. Methods: We sought to evaluate the utility of the combined assessment of Galectin 3 (Gal-3) and Galectin 3 binding protein (Gal-3bp) for post-AMI risk stratification in a large, consecutive population of AMI patients. The primary outcomes were: Recurrent angina/AMI and all-cause mortality at 12 months after the index event. Results: In total, 469 patients were included. The median Gal-3bp was 9.1 μg/mL (IQR 5.8–13.5 μg/mL), while median Gal-3 was 9.8 ng/mL (IQR 7.8–12.8 ng/mL). During the 12 month follow-up, 34 patients died and 41 had angina pectoris/reinfarction. Gal-3 was associated with all-cause mortality, while Gal-3bp correlated with the risk of angina/myocardial infarction even when corrected for other significant covariates. The final multivariable model for mortality prediction included patients’ age, left ventricular ejection fraction (LVEF), Gal-3, and renal function. The ROC curve estimated for this model has an area under the curve (AUC) of 0.84 (95%CI 0.78–0.9), which was similar to the area under the ROC curve obtained using the GRACE score 1-year mortality. Conclusions: The integrated assessment of Gal-3 and Gal-3bp could be helpful in risk stratification after AMI.

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

Ultrasound (US) is not only an important diagnostic tool for the evaluation of the pancreas, but is also a fundamental imaging technique to guide percutaneous interventions for several pancreatic diseases (fluid aspiration and drainage; invasive diagnosis by means fine-needle aspiration and core-needle biopsy; tumour ablation by radiofrequency, microwaves, irreversible electroporation, cryoablation, and high-intensity focused US). Technical improvements, such as contrast media and fusion imaging, have recently increased precision and safety and reduced procedure-related complications. New treatment US techniques for the ablation of pancreatic tumours, such as contrast-enhanced US and multimodality fusion imaging, have been recently developed and have elicited a growing interest worldwide. The purpose of this article was to review the most up-to-date role of US in percutaneous procedures for pancreatic diseases.

Conventional T1- and T2-weighted magnetic resonance imaging (MRI) of the pancreas can vary significantly due to factors such as scanner differences and pulse sequence variations. This review explores T1 and T2 mapping techniques, modern MRI methods providing quantitative information about tissue relaxation times. Various T1 and T2 mapping pulse sequences are currently under investigation. Clinical and research applications of T1 and T2 mapping in the pancreas include their correlation with fibrosis, inflammation, and neoplasms. In chronic pancreatitis, T1 mapping and extracellular volume (ECV) quantification demonstrate potential as biomarkers, aiding in early diagnosis and classification. T1 mapping also shows promise in evaluating pancreatic exocrine function and detecting glucose metabolism disorders. T2* mapping is valuable in quantifying pancreatic iron, offering insights into conditions like thalassemia major. However, challenges persist, such as the lack of consensus on optimal sequences and normal values for healthy pancreas relaxometry. Large-scale studies are needed for validation, and improvements in mapping sequences are essential for widespread clinical integration. The future holds potential for mixed qualitative and quantitative models, extending the applications of relaxometry techniques to various pancreatic lesions and enhancing routine MRI protocols for pancreatic pathology diagnosis and prognosis.