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Photon-Counting CT Effects on Sensitivity for Liver Lesion Detection: A Reader Study Using Virtual Imaging.
Radiology
January 2025
From the Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Box 3808, Durham, NC 27701 (B.W.T., K.R.K., B.C.A., S.P.T., D.E.K., B.H., M.R.B., D.M., E.S., E.A.); Department of Biostatistics and Bioinformatics (N.F., S.M., A.E.) and Department of Medical Physics (W.P.S., E.S., E.A.), Duke University, Durham, NC.
Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.
View Article and Find Full Text PDFCT Predictors of Angiolymphatic Invasion in Non-Small Cell Lung Cancer 30 mm or Smaller.
Radiology
January 2025
From the Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (Q.S., P.L., J.Z.); and Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029 (Q.S., P.L., R.Y., D.F.Y., C.I.H.).
Background Angiolymphatic invasion (ALI) is an important prognostic indicator in non-small cell lung cancer (NSCLC). However, few studies focus on radiologic features for predicting ALI in patients with early-stage NSCLCs 30 mm or smaller. Purpose To identify radiologic features for predicting ALI in NSCLCs 30 mm or smaller in maximum diameter.
View Article and Find Full Text PDFA Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDF[Expert consensus on the application of coronary CTA in risk stratification, diagnosis and treatment of chronic coronary syndrome].
Zhonghua Xin Xue Guan Bing Za Zhi
January 2025
Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.
Int J Surg
December 2024
Department of Radiology, Changhai Hospital.
Background: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate a computed tomography (CT)-based, fully automated preoperative artificial intelligence (AI) model to predict EPNI in patients with PDAC.
Methods: The authors retrospectively enrolled 1065 patients from two Shanghai hospitals between June 2014 and April 2023.
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