Aaendotson8734

Pancreatic ductal adenocarcinoma (PDAC) is a genetically heterogeneous, biologically aggressive malignancy with a uniformly poor prognosis. While most pancreatic cancers arise sporadically, a small subset of PDACs develop in patients with hereditary and familial predisposition. Detailed studies of the rare hereditary syndromes have led to identification of specific genetic abnormalities that contribute to malignancy. For example, germline mutations involving BRCA1, BRCA2, PRSS1, and mismatch repair genes predispose patients to PDAC. While patients with Lynch syndrome develop a rare "medullary" variant of adenocarcinoma, intraductal papillary mucinous tumors are observed in patients with McCune-Albright syndrome. It is now well established that PDACs originate via a multistep progression from microscopic and macroscopic precursors due to cumulative genetic abnormalities. Improved knowledge of tumor genetics and oncologic pathways has contributed to a better understanding of tumor biology with attendant implications on diagnosis, management, and prognosis. In this article, the genetic landscape of PDAC and its precursors will be described, the hereditary syndromes that predispose to PDAC will be reviewed, and the current role of imaging in screening and staging assessment, as well as the potential role of molecular tumor-targeted imaging for evaluation of patients with PDAC and its precursors, will be discussed. Keywords Abdomen/GI, Genetic Defects, Oncology, Pancreas Supplemental material is available for this article. © RSNA, 2020.

To validate the contrast agent-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm for accurate diagnosis of hepatocellular carcinoma (HCC) and categorization of all nodules encountered in patients at risk for HCC.

A single-center retrospective review of 196 nodules in 184 patients at risk for HCC (consisting of 139 HCCs, 18 non-HCC malignancies, and 39 benign nodules) was performed in a three-reader blinded read format, with the use of the CEUS LI-RADS algorithm. Pathologic confirmation was available for 143 nodules (122 HCCs, 18 non-HCC malignancies, and three benign nodules). Nodule sizes ranged between 1.0 and 16.2 cm. Nodules assessed with contrast-enhanced US were assigned various CEUS LI-RADS categories by three blinded readers. CEUS LI-RADS categorization was then compared against histopathologic findings, concurrent CT, and/or MR images or follow-up imaging to assess diagnostic accuracy of CEUS LI-RADS. In addition, the proportion of HCC in all LI-RADS (LR) categories, unterized as category LR-M.Keywords Abdomen/GI, Evidence Based Medicine, Liver, Neoplasms-Primary, Ultrasound-Contrast© RSNA, 2020.

Coronavirus disease 2019 (COVID-19) has spread quickly throughout the United States (US) causing significant disruption in healthcare and society. Tools to identify hot spots are important for public health planning. The goal of our study was to determine if natural language processing (NLP) algorithm assessment of thoracic computed tomography (CT) imaging reports correlated with the incidence of official COVID-19 cases in the US.

Using de-identified HIPAA compliant patient data from our common imaging platform interconnected with over 2,100 facilities covering all 50 states, we developed three NLP algorithms to track positive CT imaging features of respiratory illness typical in SARS-CoV-2 viral infection. We compared our findings against the number of official COVID-19 daily, weekly and state-wide.

The NLP algorithms were applied to 450,114 patient chest CT comprehensive reports gathered from January 1

to October 3

, 2020. The best performing NLP model exhibited strong correlation with daily official COVID-19 cases (r

=0.82, p<0.005). The NLP models demonstrated an early rise in cases followed by the increase of official cases, suggesting the possibility of an early predictive marker, with strong correlation to official cases on a weekly basis (r

=0.91, p<0.005). There was also substantial correlation between the NLP and official COVID-19 incidence by state (r

=0.92, p<0.005).

Using big data, we developed a novel machine-learning based NLP algorithm that can track imaging findings of respiratory illness detected on chest CT imaging reports with strong correlation with the progression of the COVID-19 pandemic in the US.

Using big data, we developed a novel machine-learning based NLP algorithm that can track imaging findings of respiratory illness detected on chest CT imaging reports with strong correlation with the progression of the COVID-19 pandemic in the US.Coronary CT angiography (CCTA) has evolved into a first-line diagnostic test for the investigation of chest pain. Despite advances toward standardizing the reporting of CCTA through the Coronary Artery Disease Reporting and Data System (or CAD-RADS) tool, the prognostic value of CCTA in the earliest stages of atherosclerosis remains limited. Translational work on the bidirectional interplay between the coronary arteries and the perivascular adipose tissue (PVAT) has highlighted PVAT as an in vivo molecular sensor of coronary inflammation. Coronary inflammation is dynamically associated with phenotypic changes in its adjacent PVAT, which can now be detected as perivascular attenuation gradients at CCTA. These gradients are captured and quantified through the fat attenuation index (FAI), a CCTA-based biomarker of coronary inflammation. FAI carries significant prognostic value in both primary and secondary prevention (patients with and without established coronary artery disease) and offers a significant improvement in cardiac risk discrimination beyond traditional risk factors, such as coronary calcium, high-risk plaque features, or the extent of coronary atherosclerosis. selleckchem Thanks to its dynamic nature, FAI may be used as a marker of disease activity, with observational studies further suggesting that it tracks the response to anti-inflammatory interventions. Finally, radiotranscriptomic studies have revealed complementary radiomic patterns of PVAT, which detect more permanent adverse fibrotic and vascular PVAT remodeling, further expanding the value of PVAT phenotyping as an important readout in modern CCTA analysis. © RSNA, 2021.