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Introduction: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans. Methods: A total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed. Results: For a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis. Discussion: This paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC.
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Objectives: The objective of this study was to assess the mechanical performance of the Lifetech IBS Angel stents during overdilation as is often required in pediatric applications; including time of first fracture, foreshortening, and the type of fracturing that occurs. Materials and methods: In vitro testing was performed and repeated for each stent three times under physiologic conditions with continuous audiovisual imaging allowing for post-testing evaluations. Assessment of sheath fit was also completed. Results: A total of 47 stents on monorail system were overdilated to complete fracture after passing through either a 4 or 5 French sheath. First strut fracture occurred in 4 and 6 mm stents when they reached greater than 50% overexpansion. Larger stents could achieve at least 30% increased diameter prior to first strut fracture. No fragmentation of any of the stents was seen throughout testing. Conclusion: The IBS Angel has thin struts allowing for a lower profile with increased maneuverability and use with smaller sheaths. Embolization potential of strut fragments was not seen. Increased diameter well beyond design parameters was seen in all with acceptable foreshortening.
