34 results match your criteria: "Calif (C.R.); and University of Northern British Columbia[Affiliation]"

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Radiol Artif Intell

September 2024

From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.).

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings.

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Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Radiol Artif Intell

July 2024

From the Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Mass (A.B., Z.Y., Y.Z., A.Z., H.H., R.C., H.J.W.L.A., B.H.K.); Department of Radiation Oncology (A.B., Z.Y., M.C.T., Y.Z., A.Z., H.H., R.C., K.X.L., D.A.H.K., H.J.W.L.A., B.H.K.) and Department of Radiology (H.J.W.L.A.), Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02115; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.P.P., S.V., T.Y.P.); Department of Biostatistics and Computational Biology, Harvard T.H. Chan School of Public Health, Boston, Mass (P.J.C.); Center for Data-Driven Discovery in Biomedicine (D3b) (A.N., A.C.R.) and Department of Neurosurgery (A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California, San Francisco, San Francisco, Calif (S.M.); and Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.).

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium ( = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center ( = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests.

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Impact of Smoking on Coronary Volume-to-Myocardial Mass Ratio: An ADVANCE Registry Substudy.

Radiol Cardiothorac Imaging

April 2024

From the Department of Radiology (K.R.H., G.S.G., J.A.L., S.L.S.) and Centre for Heart Lung Innovation & Providence Research (G.S.G., J.A.L., S.L.S.), St Paul's Hospital and University of British Columbia, 1081 Burrard St, Vancouver, BC, Canada V6Z 1Y6; Liverpool Heart and Chest Hospital, Liverpool, England (T.A.F.); Department of Radiology, Duke University School of Medicine, Durham, NC (L.H.K., M.R.P.); Wakayama Medical University, Wakayama, Japan (H.M., T. Akasaka, H.K.); Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark (B.L.N., J.M.J.); Department of Cardiology, University Hospital of Southern Denmark, Esbjerg, Denmark (N.P.R.S.); Department of Regional Health Research, University of Southern Denmark, Esbjerg, Denmark (N.P.R.S.); Erasmus Medical Center, Rotterdam, the Netherlands (K.N.); Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands (J.J.B.); Centro Cardiologico Monzino, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), University of Milan, Milan, Italy (G.P.); William Beaumont Hospital, Royal Oak, Mich (K.M.C.); Loyola University Medical Center, Maywood, Ill (M.G.R.); Aichi Medical University, Aichi, Japan (T. Amano); Department of Cardiology, Shin Koga Hospital, Fukuoka, Japan (T.K.); HeartFlow, Redwood City, Calif (C.R.); and University of Northern British Columbia, Prince George, British Columbia, Canada (G.W.P.).

Article Synopsis
  • The study investigated how smoking status affects the coronary volume-to-myocardial mass ratio (V/M) in individuals with coronary artery disease (CAD) who underwent CT analysis.
  • It included a sample of 2,874 participants, revealing that former smokers had higher coronary volume than never-smokers, while current smokers had greater myocardial mass but both groups exhibited lower V/M ratios compared to never-smokers.
  • The findings suggest that both current and former smoking status are significant predictors of lower V/M, alongside other factors like diabetes and severity of coronary stenosis.
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Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Radiol Artif Intell

May 2024

From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, = 214 [113 (52.8%) male; 104 (48.

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Impact of Coronary CT Angiography-derived Fractional Flow Reserve on Downstream Management and Clinical Outcomes in Individuals with and without Diabetes.

Radiol Cardiothorac Imaging

October 2023

From the Department of Medicine and Radiology, University of British Columbia, 1081 Burrard St, Vancouver, BC, Canada V6T 1Z3 (G.S.G., G.T., K.R.H., H.T., S.L.S., P.B., J.A.L.); Department of Cardiovascular Sciences, University of Leicester and the NIHR Biomedical Research Centre, Glenfield Hospital, Leicester, UK (G.S.G.); Department of Heart Vessels, Cardiology Service, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (G.T.); Centre for Heart Lung Innovation, University of British Columbia and St Paul's Hospital, Vancouver, BC, Canada (S.L.S., J.A.L.); Division of Cardiology, Department of Medicine, Duke University Medical Center, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (L.M.H.K., M.R.P.); Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark (B.L.N., J.J.); Department of Cardiology, Loyola University of Chicago, Chicago, Ill; (M.G.R.); Department of Cardiology, Edward Hines Jr VA Hospital, Hines, Ill (M.G.R.); Department of Cardiology, Centro Cardiologico Monzino, Milan, Italy (G.P.); Department of Cardiology, University of Liverpool, Liverpool Heart and Chest Hospital, Liverpool, UK (T.A.F.); Department of Cardiology, Beaumont Health, Royal Oak, Mich (K.M.C.); Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (P.S.D.); Department of Cardiology, Gifu Heart Center, Gifu, Japan (H.M.); Cardiac Research Unit, Institute of Regional Health Research, University Hospital of SouthWest DK, University of Southern Denmark, Odense, Denmark (N.P.R.S.); Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands (K.N.); Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands (J.J.B.); Department of Cardiology, Aichi Medical University, Aichi, Japan (T. Amano); Cardiovascular Center, Shin Koga Hospital, Fukuoka, Japan (T.K.); Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan (T. Akasaka); HeartFlow Inc, Redwood City, Calif (W.H., C.R., S.M.); Division of Nuclear Imaging, Department of Imaging, Cedars-Sinai Heart Institute, Los Angeles, Calif (D.S.B.); and Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium (B.D.B.).

Article Synopsis
  • - This study aimed to evaluate how coronary CT angiography (CCTA) and derived fractional flow reserve (FFR) are used clinically to assess coronary artery disease (CAD) in patients with diabetes mellitus (DM) compared to those without DM.
  • - The analysis included 4,290 participants and found that patients with DM tended to have more severe CAD conditions, but both groups shared similar rates of treatment changes based on CT-FFR results and coronary revascularization.
  • - Ultimately, while DM was linked to higher risk of adverse cardiovascular events over a year, it didn't significantly increase risk when accounting for the severity of arterial blockages.
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Prognostic Value of Coronary CT Angiography-derived Fractional Flow Reserve on 3-year Outcomes in Patients with Stable Angina.

Radiology

September 2023

From the Department of Cardiology, University Hospital of Southern Denmark, Esbjerg, Finsensgade 35, Esbjerg DK-6700, Denmark (K.T.M., A.R., N.P.R.S.); Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark (B.L.N., J.M.J., E.L.G., H.E.B.); Department of Clinical Medicine, Faculty of Health (B.L.N., E.L.G.), and Department of Public Health, Section for Biostatistics (E.P.), Aarhus University, Aarhus, Denmark; Department of Cardiology, Odense University Hospital, Odense, Denmark (K.A.Ø., H.M.); Department of Cardiology, Liverpool Centre for Cardiovascular Science, Liverpool Heart and Chest Hospital, Liverpool, United Kingdom (T.A.F.); Departments of Cardiovascular Medicine and Radiology, Stanford University, Stanford, Calif (K.N.); Division of Cardiology, Department of Medicine, Duke University, Durham, NC (M.R.P.); HeartFlow Inc, Mountain View, Calif (C.R., S.M.); Department of Radiology, Providence Health Care, St. Paul's Hospital, University of British Columbia, Vancouver, Canada (J.L.); and Department of Regional Health Research, University of Southern Denmark, Esbjerg, Denmark (N.P.R.S.).

Background The prognostic value of coronary CT angiography (CTA)-derived fractional flow reserve (FFR) beyond 1-year outcomes and in patients with high levels of coronary artery calcium (CAC) is uncertain. Purpose To assess the prognostic value of coronary CTA-derived FFR test results on 3-year clinical outcomes in patients with coronary stenosis and among a subgroup of patients with high levels of CAC. Materials and Methods This study represents a 3-year follow-up of patients with new-onset stable angina pectoris who were consecutively enrolled in the Assessing Diagnostic Value of Noninvasive CT-FFR in Coronary Care, known as ADVANCE (ClinicalTrials.

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O-RADS US v2022: An Update from the American College of Radiology's Ovarian-Adnexal Reporting and Data System US Committee.

Radiology

September 2023

From the Department of Radiology and Biomedical Imaging and Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, 1001 Potrero Ave, 1X57, San Francisco, CA 94110 (L.M.S.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (P.J.); Department of Radiology and Radiologic Sciences, Vanderbilt University Medical Center, Nashville, Tenn (C.H.P.); Department of Obstetrics, Gynecology, and Reproductive Sciences, Larner College of Medicine at the University of Vermont, Burlington, Vt (M.M.B.P.); Department of Obstetrics and Gynecology, University Hospitals and Department of Development and Regeneration, KU Leuven, Leuven, Belgium (W.F., D.T.); Department of Medical Imaging, Sunnybrook Health Science Centre, University of Toronto, Toronto, Canada (P.G.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (Y.G.); Department of Radiology, Mayo Clinic Arizona, Phoenix, Ariz (M.D.P.); Department of Radiology, McGill University Health Centre, Montreal, Canada (C.R.); Department of Gynecologic Oncology, Kaiser Permanente Northern California, Walnut Creek, Calif (E.J.S.B.); and Department of Radiology and Radiological Sciences and Department of Obstetrics and Gynecology, Vanderbilt University College of Medicine, Nashville, Tenn (R.F.A.).

First published in 2019, the Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, enables stratification of these lesions with use of a numeric score based on morphologic features to indicate the risk of malignancy, and offers management guidance. This risk stratification system has subsequently been validated in retrospective studies and has yielded good interreader concordance, even with users of different levels of expertise. As use of the system increased, it was recognized that an update was needed to address certain clinical challenges, clarify recommendations, and incorporate emerging data from validation studies.

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RECIST 1.1 Target Lesion Categorical Response in Metastatic Renal Cell Carcinoma: A Comparison of Conventional versus Volumetric Assessment.

Radiol Imaging Cancer

September 2023

From the David Geffen School of Medicine, University of California, Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R., J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R., H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024 (A.J.G., H.J.K., H.C., B.V., J.G.).

Purpose To investigate Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) approximations of target lesion tumor burden by comparing categorical treatment response according to conventional RECIST versus actual tumor volume measurements of RECIST target lesions.

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A Multicenter Assessment of Interreader Reliability of LI-RADS Version 2018 for MRI and CT.

Radiology

June 2023

From the Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, S255, Box 0628, San Francisco, CA 94143 (C.W.H., M.A.O.); Liver Imaging Group, Department of Radiology, University of California, San Diego, San Diego, Calif (C.W.H., C.P., T.D., D.T.M., K.J.F., C.B.S.); Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (V.C., N.H.); Department of Radiology, Yonsei University, Seoul, South Korea (J.Y.C.); Department of Radiology, University of California Irvine, Orange, Calif (S.L., R.K.); Computational and Applied Statistics Laboratory, University of California San Diego, San Diego, Calif (T.W., A.G.); Department of Radiology, New York University, New York, NY (J.B.); Department of Radiology, University of Florida, Jacksonville, Fla (C.L.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L., J.W.O.); Department of Radiology, Fundación Santa Fe de Bogotá, Bogotá, Colombia (D.A.A.); Department of Radiology, University of Michigan, Ann Arbor, Mich (M.M.L., M.S.D., W.M.); Department of Radiology, Allegheny Health Network, Pittsburgh, Pa (A.R.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (S.C.L.); Department of Radiology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY (A.S.K., E.M.H.); Departments of Radiology and Medicine, Duke University Medical Center, New York, NY (M.R.B.); Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital Paolo Giaccone, Palermo, Italy (G.B.); Department of Radiology, University of California Los Angeles, Los Angeles, Calif (M.L.D.); Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada (A.T., M.C.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.F.); CEDRUL-Centro de Diagnóstico por Imagem, João Pessoa, Brazil (E.A.C.); Department of Radiology, University of California Davis, Sacramento, Calif (M.T.C., J.P.M.); Radiology Limited, Tucson, Ariz (B.K.); Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (K.M.E., V.R.S., K.B.); Department of Radiology, Naval Medical Center San Diego, San Diego, Calif (R.M.M.); University of São Paulo/Hospital Sírio-Libanês, São Paulo, Brazil (N.H.); Department of Radiology, University of Kansas, Kansas City, Kan (S.B., R.A.); Sir H. N. Reliance Foundation Hospital and Research Centre, Mumbai, India (K.G.); Department of Radiology, California Pacific Medical Center, San Francisco, Calif (C.R.K.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (A.K.); The 3rd Affiliated Hospital, Sun Yat-sen University, Guangzhou, China (J.W.); Inland Imaging, Spokane, Wash (I.C.); Sutter Medical Group, Sacramento, Calif (B.B.); Austin Health, Melbourne, Australia (M.G.); Department of Radiology, University of Washington, Seattle, Wash (G.M.C.).

Background Various limitations have impacted research evaluating reader agreement for Liver Imaging Reporting and Data System (LI-RADS). Purpose To assess reader agreement of LI-RADS in an international multicenter multireader setting using scrollable images. Materials and Methods This retrospective study used deidentified clinical multiphase CT and MRI and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted.

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Adnexal Lesion Imaging: Past, Present, and Future.

Radiology

June 2023

From the Departments of Radiology (E.A.S.) and Obstetrics and Gynecology (E.A.S., L.M.B., S.K.W.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI 53792-3252; Division of Surgery and Cancer, Imperial College London, Hammersmith Campus, London, UK (A.R.); Department of Radiology, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Hôpital Tenon, Paris, France (I.T.N.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (P.J.); Department of Imaging Sciences, University of Rochester, Rochester, NY (A.G.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A.B.S., Y.G.); Augmented Imaging Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, and Department of Radiology, McGill University, Montreal, Canada (C.R.); and Montreal Imaging Experts, Montreal, Canada (C.R.).

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation.

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Mammographic Screening in Routine Practice: Multisite Study of Digital Breast Tomosynthesis and Digital Mammography Screenings.

Radiology

May 2023

From the Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Sanford Health, Sioux Falls, SD (M.M.T.); Solis Mammography, Houston, Tex (C.R.P.); Sutter Health, Fremont, Calif (B.C.S.); Sutter Health, Palo Alto, Calif (S.Y.L.); Hologic, Marlborough, Mass (S.P., A.R.); OM1, Boston, Mass (Y.J., L.A.S.S., J.K.P.); and Department of Radiology, Advocate Caldwell Breast Center, Park Ridge, Ill (N.A.).

Background The use of digital breast tomosynthesis (DBT) is increasing over digital mammography (DM) following studies demonstrating lower recall rates (RRs) and higher cancer detection rates (CDRs). However, inconsistent interpretation of evidence on the risks and benefits of mammography has resulted in varying screening mammography recommendations. Purpose To evaluate screening outcomes among women in the United States who underwent routine DM or DBT mammographic screening.

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Distinguishing Type 1 from Type 2 Myocardial Infarction by Using CT Coronary Angiography.

Radiol Cardiothorac Imaging

October 2022

British Heart Foundation Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland (M.N.M., A.B., E.T., A.R.C., M.D., J.D.H., J.C., C.T., R.W., A.G., M.R.D., N.L.M., D.E.N., M.C.W.); Usher Institute, University of Edinburgh, Edinburgh, Scotland (A.G., N.L.M.); University Hospital Plymouth, Plymouth, England (C.R.); Faculty of Medicine, University of Southampton, Southampton, England (N.C.); University Hospital Southampton, Southampton, England (N.C.); Department of Cardiology, Milton Keynes University Hospital, School of Sciences and Medicine, University of Buckingham, Buckingham, England (A.K.); Torbay and South Devon NHS Foundation Trust, Torquay, England (D.F.); Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, Calif (P.J.S., D.D.); and Edinburgh Imaging, Queen's Medical Research Institute University of Edinburgh, Edinburgh, Scotland (D.E.N., M.C.W.).

Article Synopsis
  • The study aimed to see if CT coronary angiography (CTCA) could differentiate between type 1 and type 2 myocardial infarction based on plaque characteristics.
  • It involved analyzing data from two studies with 155 patients having type 1 and 36 having type 2 myocardial infarction, finding that type 1 patients had significantly more and different types of plaque than type 2 patients.
  • The results showed that low-attenuation plaque was a strong predictor for type 1 myocardial infarction, suggesting it could be a useful marker for distinguishing these patients from those with type 2.
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Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans.

Radiol Artif Intell

January 2022

Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.).

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI.

Materials And Methods: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training.

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O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee.

Radiology

April 2022

From the Departments of Radiology and Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372 Madison, WI 53792-3252 (E.A.S.); Service d'Imageries Radiologiques et Interventionnelles Spécialisées (IRIS), Assistance Publique Hôpitaux de Paris, Sorbonne Université, Paris, France (I.T.N.); Division of Surgery and Cancer, Imperial College London, Hammersmith Campus, London, England (A.R.); Departments of Radiology and Obstetrics and Gynecology, University of Michigan, Ann Arbor, Mich (K.E.M.); Department of Radiology, Universitätsklinikum Salzburg, PMU Salzburg, Salzburg, Austria (R.F.); Department of Radiology, University of California-San Francisco, San Francisco, Calif (P.J.); Department of Radiology, IRCM INSERM, U1194 SIRIC, Montpellier, France (S.N.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.S.S.); Department of Radiology, McGill University Health Centre, McGill University, Montreal, Canada (C.R.); and Augmented Intelligence & Precision Health Laboratory, Research Institute of McGill University Health Centre, Montreal, Canada (C.R.).

MRI plays an important role as a secondary test or problem-solving modality in the evaluation of adnexal lesions depicted at US. MRI has increased specificity compared with US, decreasing the number of false-positive diagnoses for malignancy and thereby avoiding unnecessary or over-extensive surgery in patients with benign lesions or borderline tumors, while women with possible malignancies can be expeditiously referred for oncologic surgical evaluation. The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee is an international collaborative effort formed under the direction of the American College of Radiology and includes a diverse group of experts on adnexal imaging and management who developed the O-RADS MRI risk stratification system.

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Integrating Al Algorithms into the Clinical Workflow.

Radiol Artif Intell

November 2021

Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).

Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019-October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: image delivery, quality control, a results database, results processing, results presentation and delivery, error correction, and a dashboard for performance monitoring.

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Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Radiology

March 2022

From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.).

Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected.

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Hyperpolarized C MR Spectroscopy Depicts in Vivo Effect of Exercise on Pyruvate Metabolism in Human Skeletal Muscle.

Radiology

September 2021

From the Advanced Imaging Research Center (J.M.P., C.E.H., J.M., J.C., J.R., J.L., G.D.R., A.C., C.R.M.), Department of Radiology (J.M.P., A.C., C.R.M.), Department of Neurology and Neurotherapeutics (R.G.H.), and Department of Internal Medicine (C.R.M.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8568; Department of Electrical and Computer Engineering, University of Texas at Dallas, Dallas, Tex (J.M.P.); Department of Diagnostic Imaging and Radiology, Developing Brain Institute, Children's National Hospital, Washington, DC (Z.Z.); Department of Pediatrics and Radiology, George Washington University, Washington, DC (Z.Z.); GE Healthcare, Dallas, Tex (G.D.R.); Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, Calif (T.J.); and Veterans Affairs North Texas Healthcare System, Dallas, Tex (C.R.M.).

Article Synopsis
  • - The study explores the role of pyruvate dehydrogenase (PDH) and lactate dehydrogenase in ATP production during exercise, noting that measuring PDH flux in human muscle is challenging due to various control mechanisms.
  • - Researchers used carbon 13 MRI with hyperpolarized [1-C]-pyruvate to assess PDH activation and pyruvate metabolism in sedentary adults before, during, and after exercise, linking muscle perfusion to metabolic changes.
  • - Results showed significant increases in lactate and bicarbonate production during and after exercise, demonstrating that hyperpolarized [1-C]-pyruvate MRI can effectively capture skeletal muscle metabolism in real-time.
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Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.

Radiol Imaging Cancer

April 2021

From the Departments of Radiation Oncology (K.J.L., M.N.C., C.D.J., J.W., Y.C., C.W., C.R.K., F.F.Y.), Radiology (K.J.L.), Biostatistics and Bioinformatics (J.G.), and Medicine (A.H.), Duke University School of Medicine, 2301 Erwin Rd, Durham, NC 27710; Department of Electrical and Computer Engineering, Duke University Pratt School of Engineering, Durham, NC (K.J.L.); Radiology Medical Group of Napa, Napa, Calif (M.N.C.); Department of Radiation Oncology, Columbia University School of Medicine, New York, NY (E.X.); and Inivata, Cambridge, England (G.J.).

The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generation sequencing of circulating tumor DNA (ctDNA) encode tumor-specific radiogenomic expression patterns that can be probed to study this problem. Preliminary findings are reported from a radiogenomic analysis of CT imaging, cfDNA, and ctDNA in 24 patients (median age, 64 years; range, 49-74 years) with stage III lung cancer undergoing chemoradiation on a prospective pilot study (NCT00921739) between September 2009 and September 2014.

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Association between Cardiorespiratory Fitness and Bronchiectasis at CT: A Long-term Population-based Study of Healthy Young Adults Aged 18-30 Years in the CARDIA Study.

Radiology

July 2021

From the Division of Pulmonary and Critical Care Medicine (A.A.D., Y.O., G.R.W.) and Department of Radiology (J.C.R., R.S.J.E.), Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.A.C., R.K.); Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.A.S., R.K.); Department of Radiology, University of California, San Diego, San Diego, Calif (A.Y.); Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama School of Medicine, Birmingham, Ala (M.T.D.); and Department of Medicine, Penn Presbyterian Medical Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (G.T.).

Background Protective factors against the risk of bronchiectasis are unknown. A high level of cardiorespiratory fitness is associated with a lower risk of chronic obstructive pulmonary disease. But whether fitness relates to bronchiectasis remains, to the knowledge of the authors, unknown.

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Radiology

May 2020

From OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstr 74, PF 41, 01307 Dresden, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z.); Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz Association/Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany (A.Z., S. Leger, E.G.C.T.); German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); Medical Physics Unit, McGill University, Montréal, Canada (M.V., I.E.N.); Image Response Assessment Team Core Facility, Moffitt Cancer Center, Tampa, Fla (M.A.A.); Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Harvard University, Boston, Mass (H.J.W.L.A.); Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland (V.A., A.D., H.M.); Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (A.A.); Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Md (S.A.); Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md (S.A., A.R.); Center for Biomedical image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (R.J.B., R.B., E.A.G.P.); Radiology and Nuclear Medicine, VU University Medical Centre (VUMC), Amsterdam, the Netherlands (R.B.); Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (M.B., M.Guckenberger, S.T.L.); Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (L.B., N.D., R.G., J.L., V.V.); Laboratoire d'Imagerie Translationnelle en Oncologie, Université Paris Saclay, Inserm, Institut Curie, Orsay, France (I.B., C.N., F.O.); Cancer Imaging Dept, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.J.R.C., V.G., M.M.S.); Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland (A.D.); Laboratory of Medical Information Processing (LaTIM)-team ACTION (image-guided therapeutic action in oncology), INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France (M.C.D., M.H., T.U.); Department of Radiation Oncology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (C.V.D.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.E., S.N.); Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, Mich (I.E.N., A.U.K.R.); Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, Mass (A.Y.F.); Department of Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, Fla (R.J.G.); Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (M. Götz, F.I., K.H.M.H., J.S.); The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands (P.L., R.T.H.L.); Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany (F.L., J.S.F., D.T.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (A.L.); Department of Radiation Oncology, University of California, San Francisco, Calif (O.M.); University of Geneva, Geneva, Switzerland (H.M.); Department of Electrical Engineering, Stanford University, Stanford, Calif (S.N.); Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, Calif (S.N.); Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada (A.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich (A.U.K.R.); Department of Radiation Oncology, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (N.M.S., R.J.H.M.S., L.V.v.D.); School of Engineering, Cardiff University, Cardiff, United Kingdom (E.S., P.W.); Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom (E.S.); Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany (E.G.C.T., C.R., S. Löck), Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany (E.G.C.T., C.R.); Department of Nuclear Medicine, CHU Milétrie, Poitiers, France (T.U.); Department of Radiology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (J.v.G.); GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (J.v.G.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (J.v.G.); and Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands (F.H.P.v.V.).

Article Synopsis
  • Researchers aimed to standardize a set of 174 radiomic features used in medical imaging due to challenges caused by unstandardized definitions and reference values.
  • The study was conducted in three phases, with increasing consensus on feature validity, showing significant improvement in reproducibility across different imaging modalities by the end of the process.
  • Ultimately, 169 radiomic features were successfully standardized, which could enhance clinical application and verification in imaging diagnostics.
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Radiologic, Pathologic, Clinical, and Physiologic Findings of Electronic Cigarette or Vaping Product Use-associated Lung Injury (EVALI): Evolving Knowledge and Remaining Questions.

Radiology

March 2020

From the Department of Radiology, University of California, San Diego, 200 W Arbor Dr, #8756, San Diego, CA 92013 (S.K.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (C.R.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Ariz (B.L., H.T.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.S.H.); Laboratory for Structural, Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania Medical Center, Philadelphia, Pa (A.C., F.W.W.), Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (M.L.S., J.K.); and Department of Radiology, University of Vermont Medical Center, Burlington, Vt (J.S.K.).

Proposed as a safer alternative to smoking, the use of electronic cigarettes has not proven to be innocuous. With numerous deaths, there is an increasing degree of public interest in understanding the symptoms, imaging appearances, causes of, and treatment of electronic cigarette or vaping product use-associated lung injury (EVALI). Patients with EVALI typically have a nonspecific clinical presentation characterized by a combination of respiratory, gastrointestinal, and constitutional symptoms.

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Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Editorial Board.

Radiology

March 2020

From the Department of Radiology, University of Wisconsin Madison School of Medicine and Public Health, 600 Highland Dr, Madison, WI 53792 (D.A.B., M.L.S.); Department of Radiology, New York University, New York, NY (L.M.); Department of Musculoskeletal Radiology (M.A.B.) and Institute for Technology Assessment (E.F.H.), Massachusetts General Hospital, Boston, Mass; Department of Medical Imaging, Hospital for Sick Children, University of Toronto, Toronto, Canada (B.B.E.W.); Department of Radiology, University of California-San Diego, San Diego, Calif (K.J.F.); Department of Cancer Imaging, Division of Imaging Sciences & Biomedical Engineering, Kings College London, London, England (V.J.G.); Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, Calif (C.P.H.); and Department of Radiology and Radiologic Science, The Johns Hopkins University School of Medicine, Baltimore, Md (C.R.W.).

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Objective: Centralized reminder/recall (C-R/R) by health departments using immunization information systems is more effective and cost effective than practice-based approaches for increasing childhood vaccines but has not been studied for influenza vaccination. We assessed effectiveness and cost of C-R/R for increasing childhood influenza vaccination compared with usual care.

Methods: Within Colorado (CO) and New York (NY), random samples of primary care practices (pediatric, family medicine, and health center) were selected proportionate to where children are served-65 practices (N = 54,353 children) in CO; 101 practices (N = 65,777) in NY.

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O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee.

Radiology

January 2020

From the Department of Radiology and Radiological Sciences and Department of Obstetrics and Gynecology, Vanderbilt University College of Medicine, 1161 21st Ave S, #D3300, Nashville, Tenn 37232 (R.F.A.); Department of Obstetrics and Gynecology, University Hospitals KU Leuven, Leuven, Belgium (D.T.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (L.M.S.); Department of Development and Regeneration, KU Leuven, Leuven, Belgium (W.F.); Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium (W.F.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Brookline, Mass (B.R.B.); Department of Radiology, NYU Langone Health, New York, NY (G.L.B.); Department of Obstetrics and Gynecology, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, England (T.B.); Department of Radiology, Mayo Clinic, Rochester, Minn (D.L.B.); Department of Radiology, Center for Fetal Diagnosis and Treatment, Children's Hospital of Philadelphia, Philadelphia, Pa (B.G.C.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.C.F.); Department of Obstetrics and Gynecology, New York University School of Medicine, New York, NY (S.R.G.); Department of Radiology, Johns Hopkins University, School of Medicine, Baltimore, Md (U.M.H.); Department of Radiology, Einstein Medical Center, Philadelphia, Pa (M.M.H.); Department of Radiology and Radiological Sciences, Carell Children's Hospital at Vanderbilt, Nashville, Tenn (M.H.S.); Department of Radiology, McGill University Health Centre, Montreal, Canada (C.R.); Department of Obstetrics and Gynecology, University of Wisconsin, Madison, Wis (S.L.R.); Department of Obstetrics and Gynecology, University of Connecticut School of Medicine, Farmington, Conn (B.P.W.); Department of Obstetrics and Gynecology, Mt. Sinai Hospital, University of Toronto, Toronto, Canada (W.L.W.); and Department of Medical Imaging and Department of Obstetrics and Gynecology, University of Toronto, Sunnybrook Research Institute, Toronto, Canada (P.G.).

The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system is designed to provide consistent interpretations, to decrease or eliminate ambiguity in US reports resulting in a higher probability of accuracy in assigning risk of malignancy to ovarian and other adnexal masses, and to provide a management recommendation for each risk category. It was developed by an international multidisciplinary committee sponsored by the American College of Radiology and applies the standardized reporting tool for US based on the 2018 published lexicon of the O-RADS US working group. For risk stratification, the O-RADS US system recommends six categories (O-RADS 0-5), incorporating the range of normal to high risk of malignancy.

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Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings.

Radiology

December 2019

From the Department of Radiology (M.M., J.R., F.V., R.C.N., D.M.) and Duke Advanced Imaging Laboratories (J.S., E.S.), Duke University Medical Center, 2301 Erwin Rd, Durham, NC 27710; Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany (M.M.); Section of Department of Radiology, DIBIMED, University of Palermo, Palermo, Italy (F.V.); Siemens Healthineers, Malvern, Pa (J.C.R.); and Department of Radiology, Stanford University, School of Medicine, Stanford, Calif (B.N.P.).

Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable and of limited use for prognostic clinical studies. Purpose To investigate the effect of CT radiation dose and reconstruction settings on the reproducibility of radiomic features, as well as to identify correction factors for mitigating these sources of variability. Materials and Methods This was a secondary analysis of a prospective study of metastatic liver lesions in patients who underwent staging with single-energy dual-source contrast material-enhanced staging CT between September 2011 and April 2012.

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