Gallstones grow inside the gallbladder or biliary tract. These stones can be asymptomatic or symptomatic; only gallstones with symptoms or complications are defined as gallstone disease. Based on their composition, gallstones are classified into cholesterol gallstones, which represent the predominant entity, and bilirubin ('pigment') stones. Black pigment stones can be caused by chronic haemolysis; brown pigment stones typically develop in obstructed and infected bile ducts. For treatment, localization of the gallstones in the biliary tract is more relevant than composition. Overall, up to 20% of adults develop gallstones and >20% of those develop symptoms or complications. Risk factors for gallstones are female sex, age, pregnancy, physical inactivity, obesity and overnutrition. Factors involved in metabolic syndrome increase the risk of developing gallstones and form the basis of primary prevention by lifestyle changes. Common mutations in the hepatic cholesterol transporter ABCG8 confer most of the genetic risk of developing gallstones, which accounts for ∼25% of the total risk. Diagnosis is mainly based on clinical symptoms, abdominal ultrasonography and liver biochemistry tests. Symptoms often precede the onset of the three common and potentially life-threatening complications of gallstones (acute cholecystitis, acute cholangitis and biliary pancreatitis). Although our knowledge on the genetics and pathophysiology of gallstones has expanded recently, current treatment algorithms remain predominantly invasive and are based on surgery. Hence, our future efforts should focus on novel preventive strategies to overcome the onset of gallstones in at-risk patients in particular, but also in the population in general.

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Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.

Acute appendicitis (AA) is among the most common cause of acute abdominal pain. Diagnosis of AA is challenging; a variable combination of clinical signs and symptoms has been used together with laboratory findings in several scoring systems proposed for suggesting the probability of AA and the possible subsequent management pathway. The role of imaging in the diagnosis of AA is still debated, with variable use of US, CT and MRI in different settings worldwide. Up to date, comprehensive clinical guidelines for diagnosis and management of AA have never been issued. In July 2015, during the 3rd World Congress of the WSES, held in Jerusalem (Israel), a panel of experts including an Organizational Committee and Scientific Committee and Scientific Secretariat, participated to a Consensus Conference where eight panelists presented a number of statements developed for each of the eight main questions about diagnosis and management of AA. The statements were then voted, eventually modified and finally approved by the participants to The Consensus Conference and lately by the board of co-authors. The current paper is reporting the definitive Guidelines Statements on each of the following topics: 1) Diagnostic efficiency of clinical scoring systems, 2) Role of Imaging, 3) Non-operative treatment for uncomplicated appendicitis, 4) Timing of appendectomy and in-hospital delay, 5) Surgical treatment 6) Scoring systems for intra-operative grading of appendicitis and their clinical usefulness 7) Non-surgical treatment for complicated appendicitis: abscess or phlegmon 8) Pre-operative and post-operative antibiotics.