ABSTRACT
OBJECTIVES: To describe the MR features and prognosis of patients with an uncommon complication of primary sclerosing cholangitis (PSC) characterized by a spontaneous perforation of the common bile duct (CBD) resulting in a peri-biliary collection and a pseudo-cystic appearance of the CBD. METHODS: A single-center cohort of 263 patients with PSC who had at least two MRIs between 2003 and 2022 and a minimum follow-up of 1 year was retrospectively analyzed. MRI data (characteristics of CBD perforation and MR features of PSC) and clinical data were assessed. Analysis of survival without liver transplantation according to type of PSC (classical or CBD spontaneous perforation) was performed according to the Kaplan-Meier method and the curves were compared using the Log-Rank test. RESULTS: A total of nine (3.4%) PSC patients (5 males) had perforation of the CBD with a median age at diagnosis of 18 years compared to 33 years for the control group (p = 0.019). The peri-biliary collections were variable in appearance (fusiform or pedunculated), with a diameter ranging from 5 to 54 mm. All nine patients showed intra- and extra-hepatic bile duct involvement, dysmorphia, and high ANALI scores. The clinical course was characterized by numerous complications in most patients, and five patients (56%) underwent liver transplantation at a median time of 5 years from diagnosis, compared to 40 patients (16%) in the control group (p = 0.02). CONCLUSION: The spontaneous perforation of the common bile duct is an uncommon complication of primary sclerosing cholangitis that affects young patients and is associated with a poor prognosis. CLINICAL RELEVANCE STATEMENT: This uncommon complication of primary sclerosing cholangitis with perforation of the common bile duct resulting in a peri-biliary collection and a pseudo-cystic appearance of the common bile duct is characterized by a poor prognosis in younger patients. KEY POINTS: ⢠Among 263 patients with primary sclerosing cholangitis (PSC), nine patients (3.6%) had an uncommon complication characterized on MRI by perforation of the common bile duct (CBD). ⢠This perforation of the CBD was responsible in all nine cases for the formation of a peri-biliary collection, giving a pseudo-cystic appearance to the CBD. ⢠The spontaneous perforation of the common bile duct is an uncommon complication of primary sclerosing cholangitis that affects young patients with a poor prognosis.
ABSTRACT
Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.
