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Highlights Intestinal malrotation is uncommon in adulthood and is often missed. Computed tomography scan of the abdomen is the diagnostic tool of choice. Prompt intervention by surgical exploration is crucial for patient survival. Laparoscopy is challenging in diagnosed volvulus or acute bowel obstruction. Correcting asymptomatic malrotation after 20-years-old is not recommended.

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Purpose: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability.Methods: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared.Results: Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [P < 0.0001]).Conclusions: Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

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Introduction: There is a pressing need for the development of new devices facilitating advanced minimally invasive approaches for the in situ treatment of pancreatic cancer (PaCa). To this end, a new endoscopic ultrasound (EUS) compatible cryocatheter (Frostbite) has been developed. When paired with the novel Pressurized Subcooled Nitrogen (PSN) cryoconsole, a cryogen is circulated within EUS cryocatheter delivering ultracold ablative temperatures to a targeted tissue in a closed loop manner. In this study, we evaluated cryocatheter performance for its potential use in transesophageal in situ ablation of PaCa and liver cancer. Methods: A ;1m cryocatheter with a 13 cm long 17 gauge needle with a 3cm ablation tip, was connected to PSN and then passed through the working channel of a EUS endoscope. Performance evaluations included a 37°C ultrasound gel model, ex vivo tissue engineered PaCa model and an acute porcine study wherein 6 lesions were created within the liver (under IACUC Approval). Performance assessment included measurement of ice ball size, isotherm profile in real time and destruction area created. A single 5-minute freeze protocol was employed for all evaluations. Results: Bench studies demonstrated the generation of a 2.4cm diameter iceball with a tip temperature of , -170°C and penetration of the -40 and -20°C isotherms to 1.5cm and 2.1cm (respectively) following 5mins. Analysis of tissue destruction using PaCa tumor model revealed the creation of a 2.1cm ablation area 1 day post freeze. The porcine study demonstrated the consistent generation of a 2cm x 3.1cm (diameter x length) ablation zone following a single 5 minute freeze protocol. The porcine study also demonstrated the ability to deliver targeted destruction of tissue in close proximity to major vasculature without damaging the blood vessel. Conclusion:The results of this study demonstrated that the cryocatheter was able to rapidly and effectively freeze targeted tissue via a EUS approach. The results showed the device was able to consistently ablate a 2cm x 3cm area using a single 5 minute freeze protocol across all models. Analysis of the ablation efficacy revealed ;70% destruction within the overall frozen mass compared to , 40% attained with current percutaneous based cryodevices. Although further testing and refinement are needed, these studies demonstrated the potential of this new approach to provide a next-generation strategy for the treatment of PaCa.

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