Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Purpose: To evaluate the feasibility of using three‐dimensional (3D) MR elastography (MRE) to determine the stiffness of the pancreas in healthy volunteers.Materials and Methods: Twenty healthy volunteers underwent 1.5 Tesla MRE exams using an accelerated echo planar imaging (EPI) pulse sequence with low‐frequency vibrations (40 and 60 Hz). Stiffness was calculated with a 3D direct inversion algorithm. The mean shear stiffness in five pancreatic subregions (uncinate, head, neck, body, and tail) and the corresponding liver stiffness were calculated. The intrasubject coefficient of variation (CV) was calculated as a measure of the reproducibility for each volunteer.Results: The mean shear stiffness (average of values obtained in different pancreatic subregions) was (1.15 ± 0.17) kPa at 40 Hz, and (2.09 ± 0.33) kPa at 60 Hz. The corresponding liver stiffness was higher than the pancreas stiffness at 40 Hz ([1.60 ± 0.21] kPa, mean pancreas‐to‐liver stiffness ratio: 0.72), but similar at 60Hz ([2.12 ± 0.23) kPa, mean ratio: 0.95). The mean intrasubject CV for each pancreatic subregion was lower at 40 Hz than 60 Hz (P < 0.05 for all subregions, range: 11.9–15.7% at 40 Hz and 16.5–19.6% at 60 Hz).Conclusion: The 3D pancreatic MRE can provide promising and reproducible stiffness measurements throughout the pancreas, with more consistent data acquired at 40 Hz. J. Magn. Reson. Imaging 2015;41:369–375.© 2013 Wiley Periodicals, Inc.

For the diagnosis of nonadvanced fibrosis (fibrosis stage ≤2), inflammation should be taken into account as a confounding factor that increases hepatic stiffness, potentially causing overestimation... PurposeTo determine the diagnostic performance of magnetic resonance (MR) elastography for the staging of hepatic fibrosis and to evaluate the influence of necroinflammation on hepatic stiffness in...

Three-dimensional MR elastography is useful for comprehensively detecting early necroinflammation, distinguishing necroinflammation from fibrosis, and gauging the severity of portal hypertension in... Background The value of measuring mechanical properties to categorize various pathophysiologic states of the liver is as yet undetermined in chronic hepatitis B (CHB) or C (CHC). Purpose To evaluat...

The American Journal of Gastroenterology is published by Nature Publishing Group (NPG) on behalf of the American College of Gastroenterology (ACG). Ranked the #1 clinical journal covering gastroenterology and hepatology*, The American Journal of Gastroenterology (AJG) provides practical and professional support for clinicians dealing with the gastroenterological disorders seen most often in patients. Published with practicing clinicians in mind, the journal aims to be easily accessible, organizing its content by topic, both online and in print. www.amjgastro.com, *2007 Journal Citation Report (Thomson Reuters, 2008)