Clinical Imaging最新文献

{"title":"Comment on “Diagnostic accuracy and limitations of intravoxel incoherent motion diffusion-weighted imaging for differentiating breast tumors: A systematic review and meta-analysis”","authors":"Amit Kumar Gupta , Prashant Ramdas Kokiwar , Janvi Patel , Archana Dhyani","doi":"10.1016/j.clinimag.2026.110740","DOIUrl":"10.1016/j.clinimag.2026.110740","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"132 ","pages":"Article 110740"},"PeriodicalIF":1.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world use of PACS-integrated automated spine numbering in MRI","authors":"Young Son ,&nbsp;Bio Joo ,&nbsp;Mina Park ,&nbsp;Sung Jun Ahn ,&nbsp;Sungjun Kim ,&nbsp;Hong-Seon Lee","doi":"10.1016/j.clinimag.2026.110744","DOIUrl":"10.1016/j.clinimag.2026.110744","url":null,"abstract":"<div><h3>Purpose</h3><div>Traditional methods of vertebral identification have predominantly relied on relative approaches, depending on discernible landmarks. Artificial Intelligence (AI) has emerged as a transformative force in radiology, aiming to augment the workflow of radiologists and the benefit of patients. This study aims to investigate the real-world application of picture archiving and communication system (PACS)-integrated automated spine numbering for the daily interpretation of spinal magnetic resonance imaging (MRI) scans.</div></div><div><h3>Methods</h3><div>This retrospective study, at a tertiary hospital, analyzed 235 spine MRI cases from November 2023 to January 2024. The study focused on the effect of AI-assisted spine labeling system. We measured reading times from PACS log records, leading to the exclusion of 32 cases due to time outliers. Thus, 109 (53.7%) implemented AI, while 94 (46.3%) did not. Subgroup analysis evaluated differences based on the type of radiologist (specialist vs. resident), whether the examination was an initial or follow-up, and the anatomic region (lumbar vs. non-lumbar).</div></div><div><h3>Results</h3><div>Integrating an AI-assisted spine labeling algorithm into the PACS significantly reduced reading times for residents (<em>p</em> &lt; 0.05) but not for specialists. AI-implemented cases demonstrated high accuracy, with only 2.8% discordance. Despite AI implementation, overall reading times did not differ significantly (<em>p</em> = 0.0858).</div></div><div><h3>Conclusion</h3><div>AI has the potential to enhance efficiency, particularly benefiting trainees, by providing a consistent reference for the spinal anatomy. Future studies should explore the effect of AI on clinical outcomes and patient care.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"132 ","pages":"Article 110744"},"PeriodicalIF":1.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automation bias and overconfidence in artificial intelligence and associated legal implications","authors":"Jonathan L. Mezrich","doi":"10.1016/j.clinimag.2026.110745","DOIUrl":"10.1016/j.clinimag.2026.110745","url":null,"abstract":"<div><div>As use of artificial intelligence algorithms in clinical practice becomes more commonplace, radiologists may become overconfident in the abilities of these applications or succumb to automation bias. However misplaced confidence in artificial intelligence may have legal implications. This article makes a number of recommendations for radiologists to help temper biases and limit liability as algorithms become more prevalent in practice.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"132 ","pages":"Article 110745"},"PeriodicalIF":1.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on “Classifying the clinical significance of common breast pain symptoms using a large language model, ChatGPT (GPT-4)”","authors":"Yihan Hu","doi":"10.1016/j.clinimag.2026.110741","DOIUrl":"10.1016/j.clinimag.2026.110741","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"132 ","pages":"Article 110741"},"PeriodicalIF":1.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply - artificial intelligence and its concerning impact on training.","authors":"Jonathan L Mezrich","doi":"10.1016/j.clinimag.2026.110784","DOIUrl":"https://doi.org/10.1016/j.clinimag.2026.110784","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":" ","pages":"110784"},"PeriodicalIF":1.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and automation bias: A perspective from residency training.","authors":"Nicolás Honores","doi":"10.1016/j.clinimag.2026.110785","DOIUrl":"https://doi.org/10.1016/j.clinimag.2026.110785","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":" ","pages":"110785"},"PeriodicalIF":1.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gender diversity in academic rank and tenure among E-ROAD medical specialties","authors":"Matthew Joseph Lynn ,&nbsp;Jasmine Memar-Vaghri ,&nbsp;Mansimran Virk ,&nbsp;Shurjeel Uddin Qazi ,&nbsp;Jeffrey Ding ,&nbsp;Faisal Khosa","doi":"10.1016/j.clinimag.2026.110733","DOIUrl":"10.1016/j.clinimag.2026.110733","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>Gender diversity in academic medicine has increased over time, but it is unknown whether these trends vary by academic rank and tenure status in highly competitive specialties. The aim of this study was to analyze trends in gender representation among emergency medicine, radiology, ophthalmology, anesthesiology, and dermatology (E-ROAD) faculty.</div></div><div><h3>Materials and methods</h3><div>Publicly available data from the Association of American Medical Colleges (AAMC) Faculty Roster (1966–2021) were analyzed. Variables included gender and academic rank. Significance testing for 2-group comparisons was conducted via Mann Whitney <em>U</em> test.</div></div><div><h3>Results</h3><div>Representation of women increased steadily across all E-ROAD specialties and ranks. The largest shifts occurred among professors and tenured positions. From 2001 to 2021, women outnumbered men among assistant professors and instructors in dermatology. Faculty approached gender parity (man-to-woman ratio &lt; 2) among associate professors in dermatology, assistant professors in all specialties except radiology, and instructors across all specialties. In dermatology, women outnumbered men across on-track, not-on-track, and tenure-unavailable positions. Faculty approached gender parity among on-track faculty in anesthesiology and ophthalmology, and among not-on-track faculty in all specialties except radiology. By 2021, men outnumbered women professors by 2.7 to 5.2-fold across E-ROAD specialties.</div></div><div><h3>Conclusions</h3><div>Representation of women increased significantly across all academic ranks and tenure statuses in E-ROAD specialties, with especially large gains in senior ranks. However, men continue to hold the majority of professor positions. Junior ranks and non-tenured positions approached gender parity in most specialties by 2021.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"131 ","pages":"Article 110733"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of lung-RADS 2022 and Fleischner’s 2017 guidelines","authors":"Dyutika Kantamneni ,&nbsp;Kathleen M. Capaccione ,&nbsp;Daniel Amin ,&nbsp;Mary M. Salvatore","doi":"10.1016/j.clinimag.2026.110730","DOIUrl":"10.1016/j.clinimag.2026.110730","url":null,"abstract":"<div><div>Pulmonary nodules are among the most frequent findings on chest CT scans, creating challenges in balancing early detection with the avoidance of overdiagnosis. This brief communication compares the Fleischner Society 2017 and Lung-RADS 2022 guidelines for management of single solid nodules in high-risk adults. Although these guidelines are designed for distinct clinical contexts, high-risk individuals often undergo diagnostic CT scans outside screening pathways, resulting in variation in follow-up strategies and management. We conducted a focused comparative analysis of both systems, examining follow-up recommendations for solid nodules ≤5 mm, 8 mm, and ≥9 mm. Both systems mostly align for nodules ≤5 mm, while Lung-RADS recommends earlier PET/CT evaluation for nodules &gt;8 mm and a higher threshold for biopsy than Fleischner. These differences can influence surveillance intensity, timing of diagnosis, and resource utilization when screening eligible patients undergo diagnostic CT outside structured LDCT programs. Recognizing that this ambiguity arises from clinical workflow rather than from conceptual conflict between guidelines may help radiologists apply each framework more appropriately and reduce uncertainty in high-risk patients encountered outside formal screening pathways.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"131 ","pages":"Article 110730"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic accuracy of elastography in differentiating parathyroid lesions from cervical tissues: A systematic review and meta-analysis","authors":"Amir Hassankhani ,&nbsp;Payam Jannatdoust ,&nbsp;Parya Valizadeh ,&nbsp;Melika Amoukhteh ,&nbsp;Abbas Mohammadi ,&nbsp;Cem Bilgin ,&nbsp;Ali Gholamrezanezhad ,&nbsp;Ali Haq","doi":"10.1016/j.clinimag.2026.110725","DOIUrl":"10.1016/j.clinimag.2026.110725","url":null,"abstract":"<div><h3>Background</h3><div>Accurate identification of parathyroid lesions is essential for diagnosis and management, yet conventional imaging techniques, including ultrasound and ^99mTc-sestamibi scintigraphy, can be discordant with the reference standard.</div></div><div><h3>Purpose</h3><div>To evaluate the diagnostic accuracy of elastography in differentiating parathyroid lesions from surrounding cervical tissues.</div></div><div><h3>Methods</h3><div>PubMed, Embase, and Scopus were searched through July 2025. Studies assessing elastography in patients with suspected or confirmed parathyroid lesions—including adenomas, hyperplasia, and carcinoma—and reporting diagnostic accuracy against histopathology or consistent follow-up were included. Pooled sensitivity, specificity, and summary receiver operating characteristic (SROC) curves were calculated using a bivariate random-effects model.</div></div><div><h3>Results</h3><div>Ten studies (739 patients) met inclusion criteria. Pooled sensitivity and specificity were 86.5% (95% CI, 79.8–91.3%) and 82.8% (95% CI, 76.0–87.9%), with an SROC area under the curve (AUC) of 0.91. Shear wave elastography outperformed strain elastography (AUC 0.94 vs 0.74, <em>p</em> = 0.009), and kPa-based stiffness measurements were superior to velocity measures (AUC 0.96 vs 0.83, <em>p</em> &lt; 0.01). Accuracy was higher when parathyroid was compared with thyroid parenchyma/muscle (AUC 0.94) than with thyroid nodules (AUC 0.89), and in secondary versus primary hyperparathyroidism (AUC 0.96 vs 0.89, <em>p</em> = 0.004). High-frequency probes (≥10 MHz) further improved performance.</div></div><div><h3>Conclusion</h3><div>Elastography, particularly shear wave elastography with kPa-based measurements, demonstrates high diagnostic accuracy for differentiating parathyroid lesions from surrounding cervical tissues and may serve as a valuable adjunct to conventional imaging. Standardized protocols and larger multicenter studies are needed to confirm its clinical utility.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"131 ","pages":"Article 110725"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonographic evaluation of cosmetic fillers: patterns and frequent complications – A literature review","authors":"Faezeh Khorasanizadeh ,&nbsp;Arman Momeni ,&nbsp;Alireza Daneshvar ,&nbsp;Rad Ghannadzadeh ,&nbsp;Ifa Etesami ,&nbsp;Ximena Wortsman","doi":"10.1016/j.clinimag.2025.110708","DOIUrl":"10.1016/j.clinimag.2025.110708","url":null,"abstract":"<div><h3>Purpose</h3><div>The use of cosmetic fillers has become increasingly popular. Radiologists and sonographers should be aware of the ultrasonographic characteristics of the most commonly used cosmetic fillers and the sonographic features of their frequent complications.</div></div><div><h3>Methods</h3><div>Ultrasound findings of hyaluronic acid (HA), poly-L-lactic acid (PLLA), calcium hydroxyapatite (CaHA), polymethylmethacrylate (PMMA), polycaprolactone (PCL), silicone oil, polyalkylimide, polyacrylamide (PAAG) and autologous fat were systematically reviewed. Immediate, early, and delayed filler complications—including vascular occlusion, cellulitis and abscess formation, panniculitis, foreign-body granulomatous reaction, fat necrosis, capsular contraction, filler migration, and overfilling—were described with their specific sonographic characteristics.</div></div><div><h3>Results</h3><div>Filler-pattern ultrasound is material-specific: HA—anechoic/hypoechoic, pseudocystic deposits; PLLA—initially hyperechoic, fading over time; PCL—a hypoechoic matrix with hyperechoic comet-tail spots; PMMA—bright echogenic deposits with comet-tail artifacts; CaHA—echoes/shadows depend on dilution/mixing; PAAG—mostly anechoic/hypoechoic and stable over time; silicone—distinctive snowstorm/posterior reverberation; autologous fat—hypoechoic nodules with possible fat necrosis. Vascular occlusion shows absent/reduced Doppler flow; Cellulitis/panniculitis show increased echogenicity, thickened septa, and edema; abscess is an anechoic/hypoechoic lesion with debris and posterior enhancement. Granulomas are hypoechoic nodules with possible calcifications or vascularity. Fat necrosis presents as oil cysts (round anechoic deposits with echogenic borders; calcifications possible). Migration is filler in abnormal locations; overfilling denotes accumulated filler; capsular contracture shows as hyperechoic capsule surrounding deposits.</div></div><div><h3>Conclusion</h3><div>High-frequency ultrasound, with color Doppler, is the first-line modality for identifying filler type, location, and complications. Routine use improves diagnostic accuracy and patient safety in esthetic medicine and guides therapeutic interventions.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"131 ","pages":"Article 110708"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}