American Journal of Roentgenology最新文献

{"title":"Transarterial Embolization for Musculoskeletal Pain Management: <i>AJR</i> Expert Panel Narrative Review.","authors":"Abin Sajan, Yan Epelboym, Ana Fernández Martínez, Mark Little, Reza Talaie, Ari Isaacson","doi":"10.2214/AJR.24.31626","DOIUrl":"10.2214/AJR.24.31626","url":null,"abstract":"<p><p>Musculoskeletal embolization has emerged in recent years as a treatment of chronic joint pain, as the inflammatory cascade responsible for such pain has become better understood. Studies have shown a complex interplay between joint inflammation and synovial hypervascularity that causes growth of new unmyelinated nerve fibers responsible for pain. Embolization targets joint hypervascularity to disrupt the inflammatory cycle and provide pain relief. The standard treatment algorithm for chronic joint pain is well established and entails escalating therapeutic options that include exercise, self-management programs, analgesic medications, intraarticular injections, and finally surgical replacement or release. Genicular artery embolization (GAE), targeting abnormal vasculature around the knee joint, is the most studied musculoskeletal embolization procedure, reflecting the high worldwide prevalence and increasing incidence of knee osteoarthritis (OA). GAE is now supported by multiple prospective studies, including randomized controlled trials comparing GAE versus sham treatment. Embolization has also extended outside of the knee joint to include the shoulder (treatment of adhesive capsulitis or secondary stiff shoulder), elbow (medial or lateral epicondylitis), hip (OA, great trochanteric pain syndrome), and ankle (plantar fasciitis). This <i>AJR</i> Expert Panel Narrative Review discusses the current status of transarterial embolization for musculoskeletal pain management, focusing on treatment of knee OA and chronic shoulder pain.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2431626"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Patient Ratings of Radiologists: Point-Why We Should Embrace Them.","authors":"Yoshimi Anzai, Troy Hutchins","doi":"10.2214/AJR.24.31690","DOIUrl":"10.2214/AJR.24.31690","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2431690"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonmass Lesions on Breast Ultrasound: Interreader Agreement and Associations With Malignancy.","authors":"Su Min Cho, Joo Hee Cha, Hak Hee Kim, Hee Jung Shin, Eun Young Chae, Woo Jung Choi, Hye Joung Eom, Hee Jeong Kim","doi":"10.2214/AJR.24.32278","DOIUrl":"10.2214/AJR.24.32278","url":null,"abstract":"<p><p><b>BACKGROUND.</b> Nonmass lesions (NMLs) on breast ultrasound lack clear definition and encompass a broad range of benign and malignant entities. Given the anticipated inclusion of NMLs in the BI-RADS 6th edition, a thorough understanding of these lesions will be critical for their optimal management. <b>OBJECTIVE.</b> The purpose of the present study was to evaluate interreader agreement for classification of lesions observed on breast ultrasound as NMLs and to identify the imaging features associated with malignancy in these lesions. <b>METHODS.</b> This retrospective study included 2007 patients (2005 women and two men; mean age, 54.0 ± 9.6 [SD] years) who underwent ultrasound-guided biopsy of 2381 breast lesions between January 2020 and December 2020. Two radiologists independently classified the lesions as masses or NMLs, using a definition of NMLs from a presentation at the Radiological Society of North America 2023 annual meeting. The radiologists attempted to reach consensus for discordant cases. Another radiologist recorded the mammographic and ultrasound characteristics of the NMLs. Pathologic outcomes for NMLs were extracted from the EHR. <b>RESULTS.</b> Interreader agreement for lesion classification (mass vs NML) was substantial (κ = 0.73) A total of 216 lesions were classified as NMLs by both readers independently; an additional 101 lesions were classified as NMLs by consensus review after initial discordance. Thus, 317 of 2381 lesions (13.3%) were classified as NMLs; initial reader discordance occurred for 101 of these 317 lesions (31.9%). A total of 133 of 317 NMLs (42.0%) were malignant, including invasive ductal carcinoma (48/133), ductal carcinoma in situ (43/133), and microinvasive ductal carcinoma (micro-IDC) (34/133). A total of 30.8% of malignant NMLs lacked correlative mammographic abnormalities. Ultrasound findings with the highest accuracy for identifying malignancy of NMLs were calcifications (65.6%), posterior shadowing (62.8%), and nonparallel orientation (59.3%). In multivariable analysis, variables showing significant independent associations with malignancy included calcifications (OR = 8.9), asymmetry (OR = 4.7), and mass (OR = 6.4) on mammography and greater size (OR = 1.03), nonparallel orientation (OR = 8.8), and posterior shadowing (OR = 6.3) on ultrasound. <b>CONCLUSION.</b> The analysis provides insights regarding reader variability for classifying ultrasound lesions as NMLs on the basis of an existing definition as well as regarding the potential utility of imaging findings for characterizing such lesions as malignant. <b>CLINICAL IMPACT.</b> These findings indicate the need for further precision and clarification regarding the definition of NMLs and for further investigation to determine which NMLs have the greatest malignancy risk.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2432278"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunistic Assessment for Parathyroid Adenoma on CT: A Retrospective Cohort Study Evaluating Primary Hyperparathyroidism-Associated Morbidity Over 10 Years of Follow-Up.","authors":"Paul M Bunch, Kevin D Hiatt, Joseph Rigdon, Leon Lenchik, Matthew A Gorris, Reese W Randle","doi":"10.2214/AJR.24.32031","DOIUrl":"10.2214/AJR.24.32031","url":null,"abstract":"<p><p><b>BACKGROUND.</b> Primary hyperparathyroidism (PHPT) is underdiagnosed. Opportunistic imaging-based parathyroid gland assessment is a proposed strategy for identifying patients at increased risk of undiagnosed PHPT. However, whether this approach is likely to identify individuals with clinically significant disease is unknown. <b>OBJECTIVE.</b> This study's objective was to assess for associations of the presence of an enlarged parathyroid gland on contrast-enhanced CT with clinical outcomes causally related to PHPT. <b>METHODS.</b> This retrospective cohort study included patients 18 years old or older with at least one contrast-enhanced chest or neck CT examination performed from January 2012 to December 2012, at least one noncontrast CT examination covering the chest or neck region without a date restriction, and at least one clinical encounter in the health system from January 2022 to December 2022. A neuroradiologist reviewed the CT examinations to determine the presence versus absence of an enlarged parathyroid gland on the 2012 study. Patient demographics, serum calcium results, and diagnosis codes for clinical outcomes causally related to PHPT were extracted from the EHR. Calcium results and diagnosis codes were classified as preexisting if documented before and as incident if documented after the 2012 contrast-enhanced CT examination. <b>RESULTS.</b> The cohort included 1198 patients (593 men and 605 women; mean age, 51.6 years), of whom 43 (3.6%) were assessed as having an enlarged parathyroid gland on the 2012 contrast-enhanced CT examination. PHPT was diagnosed in 16.3% of patients with, versus 0.3% of patients without, an enlarged parathyroid gland (<i>p</i> < .001). After adjustment for age, sex, race, and ethnicity, the presence of an enlarged parathyroid gland on contrast-enhanced CT was associated with significantly increased odds of preexisting nephrolithiasis (OR = 2.71; <i>p</i> = .03), hypercalcemia (OR = 5.30; <i>p</i> < .001), and PHPT (OR = 12.59; <i>p</i> = .008) as well as increased odds of incident osteopenia or osteoporosis (OR = 2.78; <i>p</i> = .008), nephrolithiasis (OR = 4.95; <i>p</i> < .001), hypercalcemia (OR = 7.58; <i>p</i> < .001), and PHPT (OR = 148.01; <i>p</i> < .001). <b>CONCLUSION.</b> An enlarged parathyroid gland indicated increased risk of PHPT as well as increased risk of preexisting and incident clinical conditions causally related to PHPT. <b>CLINICAL IMPACT.</b> Opportunistic CT-based assessment is a promising strategy for identifying patients at increased risk of undiagnosed PHPT; such assessment could potentially prevent some PHPT-related complications through earlier diagnosis and treatment.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2432031"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics-Based Prediction of Patient Demographic Characteristics on Chest Radiographs: Looking Beyond Deep Learning for Risk of Bias.","authors":"Hadiseh Kavandi, Pranav Kulkarni, Sean P Garin, Preetham Bachina, Vishwa S Parekh, Paul H Yi","doi":"10.2214/AJR.24.31963","DOIUrl":"10.2214/AJR.24.31963","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2431963"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Best of <i>AJR</i>, 2024.","authors":"","doi":"10.2214/AJR.24.32451","DOIUrl":"https://doi.org/10.2214/AJR.24.32451","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":"224 2","pages":"e2432451"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Comment: Inching Forward With LI-RADS Nonradiation Treatment Response Algorithm Version 2024.","authors":"Jonathan S Moulton","doi":"10.2214/AJR.24.32378","DOIUrl":"10.2214/AJR.24.32378","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2432378"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deployment of Artificial Intelligence in Radiology: Strategies for Success.","authors":"Sirui Jiang, Syed M A Bukhari, Arjun Krishnan, Kaustav Bera, Avishkar Sharma, Danielle Caovan, Beverly Rosipko, Amit Gupta","doi":"10.2214/AJR.24.31898","DOIUrl":"10.2214/AJR.24.31898","url":null,"abstract":"<p><p>Radiology, as a highly technical and information-rich medical specialty, is well suited for artificial intelligence (AI) product development, and many U.S. FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2431898"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Comment: Use of Ancillary Features in LI-RADS Treatment Response Algorithm Improves Accuracy and Decreases the Frequency of LR-TR Equivocal Category.","authors":"Victoria Chernyak","doi":"10.2214/AJR.24.32415","DOIUrl":"10.2214/AJR.24.32415","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2432415"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Year's Resolution: Let It Go-<i>AJR</i> Podcast Series on Wellness, Episode 10.","authors":"Sherry S Wang","doi":"10.2214/AJR.24.32580","DOIUrl":"10.2214/AJR.24.32580","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"e2432580"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}