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Background Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. Methods Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. Results A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975–0.994), 0.978 (95% CI 0.950–0.996), 0.967 (95% CI 0.917–1.000), and 0.953 (95% CI 0.877–1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. Conclusions The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.

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Objective:This study proposed and validated an intelligent microcatheter-shaping algorithm for interventional embolization of intracranial aneurysms.Methods:A stepwise microcatheter simulation algorithm constrained by a vessel center line was developed based on the geometry of aneurysms and parent arteries, and a collision correction factor of vessel walls was introduced to automatically calculate the optimal microcatheter path and tip shape. The efficacy of this intelligent shaping method was verified in an in vitro aneurysm model experiment.Results:The microcatheter path can be automatically generated using the intelligent microcatheter-shaping algorithm. Furthermore, the experiment verified that the delivery performance of an intelligently shaped microcatheter was excellent with 100% placement accuracy, superior to that of three pre-shaped microcatheters: straight (0°), 45°, and 90°. In three typical cases, the microcatheter could not be placed in the aneurysms successfully within 5 min with the aid of a microwire using a manual shaping scheme; however, it can be placed in the aneurysms successfully within 5 min using an intelligent microcatheter- shaping scheme, and the time of microcatheter placement in aneurysms was short.Conclusion:This intelligent microcatheter-shaping algorithm based on three-dimensional image data is effective and reasonable. This approach has advantages over standard pre-shaped microcatheters, with a potential clinical application value.

Pancreatic neurofibroma is a very rare benign neurogenic tumor unrelated to neurofibromatosis type 1 (NF-1). As the volume increases, it has the risk of malignant transformation. The surgical prognosis of pancreatic neurofibroma is good, but its preoperative imaging features are very similar to those of malignant tumors, which may affect the formulation of treatment plans. This article reports a case of giant neurofibroma of the pancreas with contrast-enhanced ultrasound (CEUS) as one of the diagnostic methods and discusses the tumor’s preoperative clinical features, laboratory examinations, and imaging features.