Background: Pancreatic cystic lesions (PCLs) are frequent and underreported incidental findings on computed tomography (CT) scans and can evolve to pancreatic cancer—the most lethal cancer, with less than 5 months of life expectancy. Objective: The aim of this study was to develop and validate an artificial deep neural network (attention gate U-Net, also named “AGNet”) for automated detection of PCLs. This kind of technology can help radiologists to cope with an increasing demand of cross-sectional imaging tests and increase the number of PCLs incidentally detected, thus increasing the early detection of pancreatic cancer. Methods: We adapted and evaluated an algorithm based on an attention gate U-Net architecture for automated detection of PCL on CTs. A total of 335 abdominal CTs with PCLs and control cases were manually segmented in 3D by 2 radiologists with over 10 years of experience in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a neural network for segmentation followed by a postprocessing pipeline that filtered the results of the network and applied some physical constraints, such as the expected position of the pancreas, to minimize the number of false positives. Results: Of 335 studies included in this study, 297 had a PCL, including serous cystadenoma, intraductal pseudopapillary mucinous neoplasia, mucinous cystic neoplasm, and pseudocysts . The Shannon Index of the chosen data set was 0.991 with an evenness of 0.902. The mean sensitivity obtained in the detection of these lesions was 93.1% (SD 0.1%), and the specificity was 81.8% (SD 0.1%). Conclusions: This study shows a good performance of an automated artificial deep neural network in the detection of PCL on both noncontrast- and contrast-enhanced abdominal CT scans.

In medical imaging, deep learning (DL) models often struggle to delineate ambiguous structures such as tumors or organ boundaries, leading to uncertainty in defining precise contours. This challenge is amplified by inter-rater variability, where experts may disagree on boundary delineations, resulting in inconsistent segmentation outcomes. Addressing these issues requires robust algorithms capable of quantifying uncertainty, standardizing annotation practices, and improving calibration to ensure reliable predictions, particularly in multi-class and multi-rater scenarios. When models are miscalibrated and overconfident, their outputs can mislead clinical decision-making, potentially influencing radiologists to over- or under-estimate malignancy risks. The CURVAS challenge (Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation) was established to address these challenges by jointly assessing uncertainty, calibration, and segmentation quality, as well as promoting clinical relevance by evaluating organ volumes while accounting for annotation variability. To support this, a dataset of 90 contrast-enhanced CT scans from University Hospital Erlangen was curated, containing pancreas, liver, and kidney segmentations annotated by three experts. This resource provides a foundation for developing and benchmarking algorithms that balance segmentation accuracy, calibration, and reliability. A quantitative analysis of the annotations shows that kidney and liver segmentations exhibit strong consistency, whereas the pancreas remains challenging, emphasizing the need for refined labeling protocols and improved training strategies.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Pancreatic cystic lesions (PCLs) are frequent and underreported incidental findings on computed tomography (CT) scans and can evolve to pancreatic cancer—the most lethal cancer, with less than 5 months of life expectancy. Objective: The aim of this study was to develop and validate an artificial deep neural network (attention gate U-Net, also named “AGNet”) for automated detection of PCLs. This kind of technology can help radiologists to cope with an increasing demand of cross-sectional imaging tests and increase the number of PCLs incidentally detected, thus increasing the early detection of pancreatic cancer. Methods: We adapted and evaluated an algorithm based on an attention gate U-Net architecture for automated detection of PCL on CTs. A total of 335 abdominal CTs with PCLs and control cases were manually segmented in 3D by 2 radiologists with over 10 years of experience in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a neural network for segmentation followed by a postprocessing pipeline that filtered the results of the network and applied some physical constraints, such as the expected position of the pancreas, to minimize the number of false positives. Results: Of 335 studies included in this study, 297 had a PCL, including serous cystadenoma, intraductal pseudopapillary mucinous neoplasia, mucinous cystic neoplasm, and pseudocysts . The Shannon Index of the chosen data set was 0.991 with an evenness of 0.902. The mean sensitivity obtained in the detection of these lesions was 93.1% (SD 0.1%), and the specificity was 81.8% (SD 0.1%). Conclusions: This study shows a good performance of an automated artificial deep neural network in the detection of PCL on both noncontrast- and contrast-enhanced abdominal CT scans.