Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2214/ajr.150.6.1241 | DOI Listing |
Oral Radiol
January 2025
Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Av. Limeira, 901, Areião, Piracicaba, SP, 13414-903, Brazil.
Objectives: To assess the influence of a handheld X-ray unit in the diagnosis of proximal caries lesions using different digital systems by comparing with a wall-mounted unit.
Methods: Radiographs of 40 human teeth were acquired using the Eagle X-ray handheld unit (Alliage, São Paulo, Brazil) set at 2.5 mA, 60 kVp and an exposure time of 0.
Radiology
January 2025
From the Department of Radiology and Research Institute of Radiology (Y.A., S.M.L., J.C., K.H.D., J.B.S.) and Department of Cardiothoracic Surgery (S.H.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea.
Background The ninth edition of the TNM classification for lung cancer revised the N2 categorization, improving patient stratification, but prognostic heterogeneity remains for the N1 category. Purpose To define the optimal size cutoff for a bulky lymph node (LN) on CT scans and to evaluate the prognostic value of bulky LN in the clinical N staging of lung cancer. Materials and Methods This retrospective study analyzed patients who underwent lobectomy or pneumonectomy for lung cancer between January 2013 and December 2021, divided into development (2016-2021) and validation (2013-2015) cohorts.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 (Y.Z., D.F.Y., C.I.H.); and Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China (Y.Z.).
Lung cancer is the leading cause of cancer deaths globally. In various trials, the ability of low-dose CT screening to diagnose early lung cancers leads to high cure rates. It is widely accepted that the potential benefits of low-dose CT screening for lung cancer outweigh the harms.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, University Hospital Halle, Ernst-Grube-Strasse 40, 06120 Halle (Saale), Germany (D.S., J.S., J.M.B.); Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany (L.K., T.W.G., R.K.); Diagnostic Imaging and Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI (K.M.M.); Department of Pediatric Radiology, Imaging and Radiation Oncology, Core-Rhode Island, Providence, RI (K.M.M.); Department of Oncology, St Jude Children's Research Hospital, Memphis, Tenn (J.E.F.); Department of Pediatric Hematology and Oncology, University Hospital Giessen-Marburg, Giessen, Germany (C.M.K., D.K.); Medical Faculty of the Martin Luther University of Halle-Wittenberg, Halle (Saale) Germany (C.M.K.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (S.Y.C.); Roswell Park Comprehensive Cancer Center, Buffalo, NY (K.M.K.); Department of Radiation Oncology, Medical Faculty of the Martin-Luther-University, Halle (Saale), Germany (T.P., D.V.); Department of Radiation Oncology, Mayo Clinic-Jacksonville, Jacksonville, Fla (B.S.H.); Department of Radio-Oncology, Medical University Vienna, Vienna, Austria (K.D.); and Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Mass (S.D.V.).
Staging of pediatric Hodgkin lymphoma is currently based on the Ann Arbor classification, incorporating the Cotswold modifications and the Lugano classification. The Cotswold modifications provide guidelines for the use of CT and MRI. The Lugano classification emphasizes the importance of CT and PET/CT in evaluating both Hodgkin lymphoma and non-Hodgkin lymphoma but focuses on adult patients.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!