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https://www.readbyqxmd.com/read/29147766/pictorial-essay-imaging-findings-following-y90-radiation-segmentectomy-for-hepatocellular-carcinoma
#1
REVIEW
Ronald A Mora, Rehan Ali, Ahmed Gabr, Nadine Abouchaleh, Ali Al Asadi, Joseph Ralph Kallini, Frank H Miller, Vahid Yaghmai, Samdeep Mouli, Bartley Thornburg, Kush Desai, Ahsun Riaz, Robert J Lewandowski, Riad Salem
Transarterial radioembolization is a novel therapy that has gained rapid clinical acceptance for the treatment of hepatocellular carcinoma (HCC). Segmental radioembolization [also termed radiation segmentectomy (RS)] is a technique that can deliver high doses (> 190 Gy) of radiation selectively to the hepatic segment(s) containing the tumor. The aim of this comprehensive review is to provide an illustrative summary of the most relevant imaging findings encountered after radiation segmentectomy. A 62-patient cohort of Child-Pugh A patients with solitary HCC < 5 cm in size was identified...
November 17, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/29135365/deep-learning-to-classify-radiology-free-text-reports
#2
Matthew C Chen, Robyn L Ball, Lingyao Yang, Nathaniel Moradzadeh, Brian E Chapman, David B Larson, Curtis P Langlotz, Timothy J Amrhein, Matthew P Lungren
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE...
November 13, 2017: Radiology
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#3
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance...
November 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29126825/artificial-intelligence-in-medical-practice-the-question-to-the-answer
#4
REVIEW
D Douglas Miller, Eric W Brown
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society - forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence...
November 7, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/29122473/automated-radiology-operative-note-communication-tool-closing-the-loop-in-musculoskeletal-imaging
#5
William Moore, Ankur Doshi, Priya Bhattacharji, Soterios Gyftopoulos, Gina Ciavarra, Danny Kim, Michael Recht
RATIONALE AND OBJECTIVES: Correlation of imaging studies and reference standard outcomes is a significant challenge in radiology. This study evaluates the effectiveness of a new communication tool by assessing the ability of this system to correctly match the imaging studies to arthroscopy reports and qualitatively assessing radiologist behavior before and after the implementation of this system. MATERIALS AND METHODS: Using a commercially available communication or educational tool and applying a novel matching rule algorithm, radiology and arthroscopy reports were matched from January 17, 2017 to March 1, 2017 based on anatomy...
November 6, 2017: Academic Radiology
https://www.readbyqxmd.com/read/29122472/can-radiologists-learn-from-airport-baggage-screening-a-survey-about-using-fictional-patients-for-quality-assurance
#6
Andrew Phelps, Andrew L Callen, Peter Marcovici, David M Naeger, John Mongan, Emily M Webb
RATIONALE AND OBJECTIVES: For both airport baggage screeners and radiologists, low target prevalence is associated with low detection rate, a phenomenon known as "prevalence effect." In airport baggage screening, the target prevalence is artificially increased with fictional weapons that are digitally superimposed on real baggage. This strategy improves the detection rate of real weapons and also allows airport supervisors to monitor screener performance. A similar strategy using fictional patients could be applied in radiology...
November 6, 2017: Academic Radiology
https://www.readbyqxmd.com/read/29102536/use-of-social-media-in-radiology-education
#7
Saad Ranginwala, Alexander J Towbin
Social media has become the dominant method of mass digital communication over the past decade. Public figures and corporations have learned how to use this new approach to deliver their messages directly to their followers. Recently, medical educators have begun to use social media as a means to deliver educational content directly to learners. The purpose of this article is to describe the benefits of using social media for medical education. Because each social media platform has different platform-specific constraints, several different popular social media networks are discussed...
November 1, 2017: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29097379/data-science-in-radiology-a-path-forward
#8
Hugo Jwl Aerts
Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues. This information can be used throughout the clinical care path to improve diagnosis and treatment planning, as well as assess treatment response...
November 2, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/29097316/quantitative-surface-analysis-of-combined-mri-and-pet-enhances-detection-of-focal-cortical-dysplasias
#9
Yee-Leng Tan, Hosung Kim, Seunghyun Lee, Tarik Tihan, Lawrence Ver Hoef, Susanne G Mueller, Anthony James Barkovich, Duan Xu, Robert Knowlton
OBJECTIVE: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features...
October 31, 2017: NeuroImage
https://www.readbyqxmd.com/read/29094803/incidence-and-radiologic-pathological-features-of-lung-cancer-in-idiopathic-pulmonary-fibrosis
#10
Yan Liu, Min Zhu, Jing Geng, Chengjun Ban, Shu Zhang, Wenhui Chen, Yanhong Ren, Xuan He, Wang Chen, Huaping Dai
OBJECTIVE: To investigate the incidence and risk factors of lung cancer in patients with idiopathic pulmonary fibrosis (IPF), and to learn the clinical, imaging and pathological features and of lung cancer in IPF. METHODS: The study population included consecutive 268 IPF patients. Of them, 46 patients had pathologically or cytologically proven lung cancer. The demographic, clinical, HRCT, and pathological features in patients with IPF and lung cancer were analyzed and compared with the patients with IPF alone...
November 2, 2017: Clinical Respiratory Journal
https://www.readbyqxmd.com/read/29093436/learning-curve-for-surgical-treatment-of-acetabular-fractures-a-retrospective-clinical-study-of-a-practical-and-theoretical-training-course
#11
Haci Bayram Tosun, Sancar Serbest, Seyit Ali Gümüştaş, Abuzer Uludag, Suat Celik
BACKGROUND Surgical treatment of acetabular fracture and the anatomic reconstruction of the hip joint are difficult to achieve due to the complex pelvic anatomy, and surgical training requires a prolonged and steep learning curve. The aim of this study was to evaluate the effects of an applied training course, including cadaveric dissection, for the surgical treatment of acetabular fractures. MATERIAL AND METHODS This retrospective study included 35 patients who underwent surgical treatment for acetabulum fractures between 2012-2016...
November 2, 2017: Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
https://www.readbyqxmd.com/read/29081155/-changes-of-brain-function-and-cognitive-function-after-carotid-artery-stenting
#12
Z X Lu, G Deng, H L Wei, G F Zhao, L Z Wen, X Chen
Objective: To investigate the effect of carotid artery stenting(CAS) on cognitive function and brain function based on changes of a battery of neuropsychological tests and magnetic resonance imaging. Methods: Thirty-three patients were included with 17 in the stent-placement group and 16 in the control group (receiving medical treatment), among whom, the unilateral or bilateral severe internal carotid artery stenosis was confirmed by cerebral vascular angiography in the department of Interventional Radiology and Vascular Surgery of Zhongda Hospital Southeast University from June 2015 to September 2016...
October 24, 2017: Zhonghua Yi Xue za Zhi [Chinese medical journal]
https://www.readbyqxmd.com/read/29079959/integrating-natural-language-processing-and-machine-learning-algorithms-to-categorize-oncologic-response-in-radiology-reports
#13
Po-Hao Chen, Hanna Zafar, Maya Galperin-Aizenberg, Tessa Cook
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer...
October 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29079248/machine-learning-for-predicting-patient-wait-times-and-appointment-delays
#14
Catherine Curtis, Chang Liu, Thomas J Bollerman, Oleg S Pianykh
Being able to accurately predict waiting times and scheduled appointment delays can increase patient satisfaction and enable staff members to more accurately assess and respond to patient flow. In this work, the authors studied the applicability of machine learning models to predict waiting times at a walk-in radiology facility (radiography) and delay times at scheduled radiology facilities (CT, MRI, and ultrasound). In the proposed models, a variety of predictors derived from data available in the radiology information system were used to predict waiting or delay times...
October 24, 2017: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29068818/simulation-based-mastery-learning-for-thoracentesis-skills-improves-patient-outcomes-a-randomized-trial
#15
Jeffrey H Barsuk, Elaine R Cohen, Mark V Williams, Jordan Scher, Sasha F Jones, Joe Feinglass, William C McGaghie, Kelly O'Hara, Diane B Wayne
PURPOSE: Physicians-in-training often perform bedside thoracenteses in academic medical centers, and complications are more common among less experienced clinicians. Simulation-based mastery learning (SBML) is one potential solution to this problem. This study evaluated the effects of a randomized trial of thoracentesis SBML on patient complications: iatrogenic pneumothorax (IP), hemothorax, and reexpansion pulmonary edema (REPE). METHOD: The authors randomized internal medicine residents to undergo thoracentesis SBML at a tertiary care academic center from December 2012 to May 2016...
October 24, 2017: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/29055610/transition-from-peer-review-to-peer-learning-experience-in-a-radiology-department
#16
Lane F Donnelly, Scott R Dorfman, Jeremy Jones, George S Bisset
PURPOSE: To describe the process by which a radiology department moved from peer review to peer collaborative improvement (PCI) and review data from the first 16 months of the PCI process. MATERIALS AND METHODS: Data from the first 16 months after PCI were reviewed: number of case reviews performed, number of learning opportunities identified, percentage yield of learning opportunities identified, type of learning opportunities identified, and comparison of the previous parameters between case randomly reviewed versus actively pushed (issues actively identified and entered)...
October 18, 2017: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29048214/validation-of-a-full-immersion-simulation-platform-for-percutaneous-nephrolithotomy-using-3d-printing-technology
#17
Ahmed Ghazi, Timothy Campbell, Rachel Melnyk, Changyong Feng, Alex Andrusco, Jonathan Stone, Erdal Erturk
INTRODUCTION AND OBJECTIVES: The restriction of resident hours with an increasing focus on patient safety and a reduced caseload has impacted surgical training. A complex and complication prone procedure such as percutaneous nephrolithotomy (PCNL) with a steep learning curve may create an unsafe environment for hands-on resident training. In this study, we validate a high fidelity, inanimate PCNL model within a full immersion simulation environment. METHODS: Anatomically correct models of the human pelvicalyceal system (PCS), kidney, and relevant adjacent structures were created using poly-vinyl alcohol (PVA) hydrogels and 3D-printed injection molds...
October 19, 2017: Journal of Endourology
https://www.readbyqxmd.com/read/29017852/an-augmented-reality-magic-mirror-as-additive-teaching-device-for-gross-anatomy
#18
Daniela Kugelmann, Leonard Stratmann, Nils Nühlen, Felix Bork, Saskia Hoffmann, Golbarg Samarbarksh, Anna Pferschy, Anna Maria von der Heide, Andreas Eimannsberger, Pascal Fallavollita, Nassir Navab, Jens Waschke
When preparing young medical students for clinical activity, it is indispensable to acquaint them with anatomical section images which enable them to use the clinical application of imaging methods A new Augmented Reality Magic Mirror (AR MM) system, which provides the advantage of a novel, interactive learning tool in addition to a regular dissection course, was therefore tested and evaluated by 880 first-year medical students as part of the macroscopic anatomy course in 2015/16 at Ludwig-Maximilians-Universität (LMU) in Munich...
October 7, 2017: Annals of Anatomy, Anatomischer Anzeiger: Official Organ of the Anatomische Gesellschaft
https://www.readbyqxmd.com/read/28989798/how-to-train-radiology-residents-to-diagnose-pulmonary-embolism-using-a-dedicated-mri-protocol
#19
Anna Nordgren Rogberg, Sven Nyrén, Eli Westerlund, Peter Lindholm
BACKGROUND: In recent years, magnetic resonance imaging (MRI) has been suggested as an alternative to computed tomography angiography (CTA) to diagnose pulmonary embolism (PE). In previous studies, only senior radiologists have been evaluated as reviewers. PURPOSE: To investigate if radiology residents can be trained to review MRI regarding PE and to determine the learning curve effects. MATERIAL AND METHODS: Four residents independently went through a training program consisting of 70 participants that had undergone steady-state free precession MRI...
September 2017: Acta Radiologica Open
https://www.readbyqxmd.com/read/28989563/generative-method-to-discover-emphysema-subtypes-with-unsupervised-learning-using-lung-macroscopic-patterns-lmps-the-mesa-copd-study
#20
Jingkuan Song, Jie Yang, Benjamin Smith, Pallavi Balte, Eric A Hoffman, R Graham Barr, Andrew F Laine, Elsa D Angelini
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally based upon the current definition of emphysema subtypes, while unsupervised learning of texture patterns enables the objective discovery of possible new radiological emphysema subtypes. In this work, we use a variant of the Latent Dirichlet Allocation (LDA) model to discover lung macroscopic patterns (LMPs) in an unsupervised way from lung regions that encode emphysematous areas...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
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