Current problems in diagnostic radiology最新文献

{"title":"Evaluation of an image-rich quiz-based iOS app as a study resource for the ABR Core exam.","authors":"Erin Gomez, Lilly Kauffman, Elliot K Fishman, Sara Raminpour","doi":"10.1067/j.cpradiol.2025.01.003","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2025.01.003","url":null,"abstract":"<p><p>The American Board of Radiology Core exam requires that trainees demonstrate knowledge of critical concepts across 12 domains spanning a range of imaging modalities and anatomic regions. Mobile apps have become popular components of medical and radiology education since the inception of smartphones. Numerous medical educational apps are accessible via smartphone devices and tablets, regardless of operating system, for medical training and learning purposes. For over two decades, CTisus has served as an informational and educational radiology website containing image-rich materials and resources dedicated to the use of body CT. We conducted a study to evaluate the perceived utility of the CTisus iQuiz app as a study resource for the American Board of Radiology Core exam. The overall rating of the app was above average with 50 % of respondents characterizing the app as \"Good\" and 29 % evaluating the app as \"Excellent.\" Further, 85 % of survey respondents found the app easy to understand and use, with related pearls deemed helpful by 75 % of participants, the video discussions found to be clear and beneficial by 79 %. Mobile apps are a valuable tool for the current generation of medical trainees, with quizzes shown to be an effective method to evaluate and enhance knowledge. The CTisus iQuiz app may benefit radiology residents studying for the ABR Core exam by providing access to image-rich, multiple choice-based self-assessments with in-depth explanations and feedback in an accessible interface, allowing for asynchronous learning and repeated practice.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mismatch in productivity calculated from wRVU metric and the actual number of images in musculoskeletal radiographic studies.","authors":"Oganes Ashikyan, Alex Zhu, Travis Browning, Cecilia Brewington, Avneesh Chhabra","doi":"10.1067/j.cpradiol.2025.01.001","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2025.01.001","url":null,"abstract":"<p><p>The work relative value unit (wRVU) measures the physician's work involved in performing a service and is commonly used to quantify physician productivity. A critical component factored in wRVUs is the time required to perform a service. In musculoskeletal radiology, this time correlates directly with the number of images produced per radiograph. The purpose of this project was to evaluate whether the actual number of acquired images matches the number of views indicated in musculoskeletal radiographs CPT code descriptions. A query of our internal database returned 76,204 musculoskeletal radiograph reports. 440 random radiographs were reviewed to evaluate variability in the number of images obtained. This sample consisted of ten studies from each of the forty-four musculoskeletal codes. We recorded the number of actual images obtained. 242 studies from the safety net health care system and 198 studies from the university associated hospitals and clinics were evaluated. Seventy-five studies (31 %) were found to have mismatched number of images among the 242 studies from the safety net health care system. Sixty-six studies (33 %) were found to have mismatched number of images among the 198 studies sample from university associated tertiary care system. There was significant difference between the extra images obtained at two different health care systems (p < 0.001). There were more studies with extra images in the safety net system compared to the university hospital. The commonly used wRVU metric has broad variability in the assessment of work productivity for musculoskeletal radiographs given the variance in the number of images obtained.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing asymmetric enhancement on breast MRI: Besting the diagnostic challenge with imaging and clinical clues.","authors":"Stephane Chartier, Jennifer Kramer, Sheryl Jordan, Alan Chiang","doi":"10.1067/j.cpradiol.2024.12.011","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.011","url":null,"abstract":"<p><p>Breast magnetic resonance imaging (MRI) has the highest sensitivity for breast cancer detection compared to other breast imaging modalities such as mammography and ultrasound. As a functional modality, it captures the increased angiogenic activity of breast cancer through gadolinium-based contrast enhancement. Normal breast tissue also enhances, albeit in distinct patterns termed background parenchymal enhancement (BPE). Asymmetric enhancement, i.e., when one breast enhances more prominently than the other, can pose a diagnostic challenge for interpreting radiologists as distinguishing suspicious nonmass enhancement (NME) versus benign asymmetric BPE can be difficult. Correlating with patient history and imaging findings can help differentiate benign versus suspicious patterns of asymmetric enhancement. We present a collection of cases illustrating clues helpful for assessing asymmetric enhancement encountered on breast MRI.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creation of nomograms that combine clinical, CT, and radiographic features to separate benign from malignant diseases using spiculation or (and) lobulation signs.","authors":"Ruoxuan Wang, Tianjie Qi","doi":"10.1067/j.cpradiol.2024.12.014","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.014","url":null,"abstract":"<p><strong>Background: </strong>Distinguishing between benign and malignant pulmonary nodules based on CT imaging features such as the spiculation sign and/or lobulation sign remains challenging and these nodules are often misinterpreted as malignant tumors. this retrospective study aimed to develop a prediction model to estimate the likelihood of benign and malignant lung nodules exhibiting spiculation and/or lobulation signs.</p><p><strong>Methods: </strong>A total of 500 patients with pulmonary nodules from June 2022 to August 2024 were retrospectively analyzed. Among them, 190 patients with spiculation sign and lobar sign or both on CT scan were included in this study. This investigation collected the clinical information, preoperative chest CT imaging characteristics, and postoperative histopathologic results from patients.Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model performance was assessed through receiver operating characteristic(ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).</p><p><strong>Results: </strong>In our study, 190 patients with pulmonary nodules underwent lung biopsy in 10 patients and surgical resection in 180 patients, of whom 53 were benign nodules and 137 were malignant nodules. When combined with the spiculation sign or (and) the lobulation sign, the vascular cluster sign, bronchial architectural distortion, bubble-like translucent area, nodule density, and CEA were found to be significant independent predictors for determining the benignity and malignancy of pulmonary nodules. The nomogram prediction model demonstrated high predictive accuracy with an area under the ROC curve (AUC) of 0.904. Furthermore, the model's calibration curve demonstrated adequate calibration. DCA confirmed the prediction model's validity.</p><p><strong>Conclusion: </strong>The model can assist clinicians in making more accurate preoperative diagnoses and in guiding clinical decision-making regarding treatment, potentially reducing unnecessary surgical interventions.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Original Article: The history of Women in Radiology (WIR) programs at two academic institutions: How we did it and how we merged best practices\" [Current Problems in Diagnostic Radiology 54 (2025) 35-39].","authors":"Stacy E Smith, Dania Daye, Carmen Alvarez, Kirti A Magudia, Catherine H Phillips, Sandra Rincon, Miriam A Bredella, Teresa Victoria","doi":"10.1067/j.cpradiol.2024.12.013","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.013","url":null,"abstract":"","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The \"pseudo-pulmonary AVM sign\": an aid to the diagnosis of histoplasmosis and differentiation from pulmonary arteriovenous malformations.","authors":"Marlee Mason-Maready, Kiran Nandalur, Said Khayyata, Sayf Al-Katib","doi":"10.1067/j.cpradiol.2024.12.003","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.003","url":null,"abstract":"<p><p>The diagnostic algorithm for histoplasmosis highlights the importance of imaging and emphasizes the role of the radiologist in the diagnostic workup. Here we describe a case series of patients with a novel sign of lung involvement in histoplasmosis which we have coined the Pseudo-Pulmonary Arteriovenous Malformation (PAVM) sign, the usage of which would help in the imaging diagnosis of histoplasmosis aid by distinguishing it from PAVMs. PAVMs carry risk for serious complications such as systemic emboli and may require treatment; whereas, histoplasmomas do not. Differentiation of histoplasmosis from other diagnoses can be made with laboratory studies, but may require bronchoscopy, biopsy, or both. Meanwhile, PAVMs should not be biopsied due to risk of bleeding. For these reasons, distinguishing PAVMs and histoplasmosis radiologically therefore greatly impacts clinical management, and it is important for radiologists to be aware of this appearance of histoplasmosis to avoid misinterpretation as PAVM and effectively inform clinical care.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How i do it: Leveraging AutoHotkey and programmable peripheral devices for high efficiency diagnostic radiology.","authors":"Ryan P Joyce","doi":"10.1067/j.cpradiol.2024.12.012","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.012","url":null,"abstract":"<p><p>This paper discusses the use of AutoHotkey (AHK) and programmable peripheral computing devices to enhance the workflow of diagnostic radiologists. Multiple features designed and coded by an emergency teleradiologist to optimize efficiency and complete redundant tasks with ease are presented. The full AutoHotkey script, which currently supports Visage PACS, PowerScribe 360, and Epic EHR, is available in the article appendix. Recommended peripheral devices and schematics for easy integration with the AutoHotkey script are provided. Downloadable peripheral device profiles for the recommended devices are available in the appendix. The combination of task automation, achieved with AutoHotkey, and the thoughtful configuration of programmable peripheral devices, providing easy access to task automations, can lead to improved ergonomics, increased efficiency, productivity, and job satisfaction.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging of gallstones and complications.","authors":"Davin J Evanson, Lana Elcic, Jennifer W Uyeda, Maria Zulfiqar","doi":"10.1067/j.cpradiol.2024.12.007","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.007","url":null,"abstract":"<p><p>Gallbladder pathologies caused by gallstones are commonly encountered in clinical practice, making accurate diagnosis critical for effective patient management. Radiologists play a key role in differentiating these conditions through imaging interpretation, ensuring that appropriate treatment is initiated. The imaging features of gallstone associated diseases are classified into various categories, such as inflammatory conditions, benign lesions, malignant tumors, and associated complications. A comprehensive understanding of these categories and their radiologic manifestations is essential for accurate diagnosis and management of gallbladder pathology. By integrating clinical knowledge with radiologic findings, clinicians and radiologists will be equipped with practical tools to identify and distinguish between different gallstone causing conditions.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.","authors":"Jabi E Shriki, Ted Selker, Kristina Crothers, Mark Deffebach, Safia Cheeney, Jeffrey Edelman, Anupama Brixey, Mark Tubay, Laura Spece, Sirish Kishore","doi":"10.1067/j.cpradiol.2024.10.039","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.10.039","url":null,"abstract":"<p><p>In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms are employed in routine clinical settings. We review the spectrum of errors that may result from computer-aided nodule detection. In our clinical practice, we have seen errors in nodule detection, nodule localization, and nodule characterization. Each of these categories are demonstrated with illustrative cases. Through these illustrative cases, readers can be more familiar with nuances and pitfalls generated by computer-aided detection software. Although computer-aided nodule detection software is rapidly advancing, radiologists still need to thoroughly review images with mindfulness of some of the errors that can be generated by AI platforms for nodule detection.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing radiologist performance.","authors":"Heidi N Keiser, Richard B Gunderman","doi":"10.1067/j.cpradiol.2024.12.009","DOIUrl":"https://doi.org/10.1067/j.cpradiol.2024.12.009","url":null,"abstract":"<p><p>Unless radiologist performance assessment is sufficiently deep, comprehensive, and balanced, it may tend to omit, obscure, or distort key aspects of the important contributions that radiologists make, with adverse consequences for employers, radiologists themselves, and above all, the patients they serve. Here we present a model of performance assessment that includes eight key dimensions, which can be tailored as appropriate to the needs of particular programs and radiologists.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}