Objective: The purpose of this study was to evaluate the sensitivity and specificity of helical CT in the detection of adenocarcinomas of the pancreas measuring 2 cm or smaller at pathologic examination.
Materials And Methods: Thin-section triple phase (20, 40, and 70 sec after the start of injection) contrast-enhanced helical CT scans of the abdomen in 18 patients with a pancreatic carcinoma that was 2 cm or smaller and 18 patients with a normal pancreas were retrospectively reviewed by two senior radiologists who specialized in oncologic abdominal imaging. Discrepancies were resolved by consensus. The observers were unaware of the clinical information. CT scans were evaluated for the presence of a pancreatic mass, bile, and pancreatic duct stricture. The location and size of tumors as determined on CT were compared with pathologic findings. The CT results were also compared with the prospective CT interpretations derived from the radiology reports and with the endoscopic sonographic reports when available.
Results: The sensitivity of thin-section triple-phase helical CT in the detection of small pancreatic masses was 77%, and the specificity was 100% for the two experienced observers. The sensitivity and specificity were 72% and 100%, respectively, for the prospective interpretations done by 10 observers. There was no correlation between the tumor size at pathology and the CT measurements.
Conclusion: Thin-section contrast-enhanced helical CT is sensitive and highly specific for the detection of pancreatic tumors measuring 2 cm or smaller. Improvement in the detection rate of this technique compared with previous techniques lies in the optimization of parenchymal enhancement during the pancreatic phase and the decrease in slice thickness.
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http://dx.doi.org/10.2214/ajr.182.3.1820619 | DOI Listing |
J Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFBMC Musculoskelet Disord
December 2024
Physical medicine & rehabilitation research center, School of medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Pompe disease is a glycogen storage disease primarily affecting striated muscles. Despite its main manifestation in muscles, patients with Pompe disease may exhibit non-muscle symptoms, such as hearing loss, suggesting potential involvement of sensory organs or the nervous system due to glycogen accumulation.
Aims: This study aimed to evaluate the presence of concomitant small and large fiber neuropathy in patients with Pompe disease.
Mol Med
December 2024
Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Metabolic syndrome (MetS) is an indicator and diverse endocrine syndrome that combines different metabolic defects with clinical, physiological, biochemical, and metabolic factors. Obesity, visceral adiposity and abdominal obesity, dyslipidemia, insulin resistance (IR), elevated blood pressure, endothelial dysfunction, and acute or chronic inflammation are the risk factors associated with MetS. Abdominal obesity, a hallmark of MetS, highlights dysfunctional fat tissue and increased risk for cardiovascular disease and diabetes.
View Article and Find Full Text PDFSci Rep
December 2024
Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China.
The experimental verification of the Newton law of gravity at small scales has been a longstanding challenge. Recently, torsion balance experiments have successfully measured gravitational force at the millimeter scale. However, testing gravity force on quantum mechanical wave function at small scales remains difficult.
View Article and Find Full Text PDFSci Rep
December 2024
GIN, IMN-UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL).
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