Clinical Imaging最新文献

{"title":"MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.","authors":"Amir Mahmoud Ahmadzadeh, Nima Broomand Lomer, Mohammad Amin Ashoobi, Girish Bathla, Houman Sotoudeh","doi":"10.1016/j.clinimag.2024.110386","DOIUrl":"https://doi.org/10.1016/j.clinimag.2024.110386","url":null,"abstract":"<p><p>We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) status. We conducted a comprehensive search across four major databases-Web of Science, PubMed, Scopus, and Embase. All the studies that assessed the performance of radiomics and/or end-to-end DL models for predicting glioma ATRX status were included. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria and the METhodological RadiomICs Score (METRICS). Pooled estimates for performance metrics were calculated. I-squared was used to assess heterogeneity, while subgroup and sensitivity analyses were performed to find its potential sources. Publication bias was assessed using Deeks' funnel plots. Seventeen and eleven studies were included in the systematic review and meta-analysis, respectively. Most of the studies had a low risk of bias and low concern for applicability according to the QUADAS-2. Also, most of them had good quality according to the METRICS. Meta-analysis showed a pooled sensitivity of 0.80 (95%CI: 0.71-0.96), a specificity of 0.82 (95%CI: 0.67-0.93), a positive diagnostic likelihood ratio (DLR) of 6.77 (95%CI: 4.67-9.82), a negative DLR of 0.15 (95%CI: 0.06-0.38), a diagnostic odds ratio of 30.36 (95%CI: 15.87-58.05), and an area under the curve (AUC) of 0.92 (95%CI: 0.89-0.94). Subgroup analysis revealed significant intergroup differences based on several factors. Radiomics models can accurately predict ATRX status in gliomas, enhancing non-invasive tumor characterization and guiding treatment strategies.</p>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"119 ","pages":"110386"},"PeriodicalIF":1.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916180","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":"Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.","authors":"Parya Valizadeh, Payam Jannatdoust, Delaram J Ghadimi, Sara Bagherieh, Amir Hassankhani, Melika Amoukhteh, Paniz Adli, Ali Gholamrezanezhad","doi":"10.1016/j.clinimag.2024.110392","DOIUrl":"https://doi.org/10.1016/j.clinimag.2024.110392","url":null,"abstract":"<p><strong>Background: </strong>Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved LNM prediction using CT and MRI, though challenges in diagnostic accuracy remain.</p><p><strong>Methods: </strong>A systematic review and meta-analysis were conducted per established guidelines, with searches across PubMed, Scopus, Web of Science, and Embase up to February 15, 2024. Studies developing CT/MRI-based radiomics and/or DL models for preoperative LNM assessment in thyroid cancer patients were included. Data were extracted and analyzed using R software.</p><p><strong>Results: </strong>Sixteen studies were analyzed. In internal validation sets, sensitivity was 81.1 % (95 % CI: 75.6 %-85.6 %) and specificity 76.4 % (95 % CI: 68.4 %-82.9 %). Training sets showed a sensitivity of 84.4 % (95 % CI: 81.5 %-87 %) and a specificity of 84.7 % (95 % CI: 74.4 %-91.4 %). The pooled AUC was 86 % for internal validation and 87 % for training. Handcrafted radiomics had a sensitivity of 79.4 % and specificity of 69.2 %, while DL models showed 80.8 % sensitivity and 78.7 % specificity. Subgroup analysis revealed that models for papillary thyroid cancer (PTC) had a pooled specificity of 76.3 %, while those including other or unspecified cancers showed 68.3 % specificity. Despite heterogeneity, significant differences (p = 0.037) were noted between models with and without clinical data.</p><p><strong>Conclusion: </strong>Radiomics and DL models show promising potential for detecting LNM in thyroid cancer, particularly in PTC. However, study heterogeneity underscores the need for further research to optimize these imaging tools.</p>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"119 ","pages":"110392"},"PeriodicalIF":1.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916209","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":"Prevalence of anomalies and variants of coronary arteries: A single center study by coronary CT angiography.","authors":"İsmail Yağmurhan Gilan, Kaan Esen, Yüksel Balcı, Ahmet Hakan Öztürk","doi":"10.1016/j.clinimag.2024.110389","DOIUrl":"https://doi.org/10.1016/j.clinimag.2024.110389","url":null,"abstract":"<p><strong>Purpose: </strong>It has been demonstrated that the coronary artery anomalies (CAAs) are generally asymptomatic. However, some cases can cause severe life threatening events. As coronary computed tomography angiography (CCTA) has emerged as a non-invasive alternative to invasive coronary angiography for the evaluation of coronary anatomy, the prevalence of CAAs in CCTA may more closely reflect the true prevalence in the general population. So we aimed to review and determine the prevalence of CAAs and variants retrospectively in patients who underwent CCTA at our center.</p><p><strong>Methods: </strong>Reports of 1802 patients who underwent CCTA at the radiology department of our university hospital were traced for CAAs. At least two independent investigators reviewed the images, which were selected for further assessment prior to final classification.</p><p><strong>Results: </strong>One hundred and fifty two anomalies in 152 patients (8.44 %) were encountered. Origin of any coronary artery from the pulmonary trunk in 1 (0.06 %), origin of LMCA from right sinus in 1 (0.06 %), origin of right coronary artery (RCA) from left sinus in 5 (0.28 %), origin of left anterior descending artery (LAD) from right sinus in 2 (0.11 %), origin of circumflex branch (RCx) of LMCA from right sinus in 6 (0.33 %), origin of RCx from RCA in 4 (0.22 %), origin of any coronary artery from the ascending aorta in 2 (0.11 %), split RCA in 5 (0.28 %), RCx and left marginal artery from the first diagonal artery in 1 (0.06 %), myocardial bridging in 123 (6.83 %) and fistula in 2 (0.11 %) were detected as CAAs.</p><p><strong>Conclusion: </strong>The prevalence of CAAs observed in this study was similar to the literature. CCTA can clearly visualize the anomalous origin, course and termination of the coronary artery.</p>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"119 ","pages":"110389"},"PeriodicalIF":1.8,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916213","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":"Optimizing ChatGPT-4's radiology performance with scale-invariant feature transform and advanced prompt engineering","authors":"Sultan Alam,&nbsp;Abdul Rahman,&nbsp;Shahab Saquib Sohail","doi":"10.1016/j.clinimag.2024.110368","DOIUrl":"10.1016/j.clinimag.2024.110368","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"118 ","pages":"Article 110368"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748248","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":"Impact factors and publication times of original scientific research in radiology journals","authors":"Lucy Y. Lei ,&nbsp;Osher N.Y. Lee ,&nbsp;Charlotte J. Yong-Hing ,&nbsp;Nicolas Murray ,&nbsp;Ismail T. Ali ,&nbsp;Adnan M. Sheikh ,&nbsp;Harneet Cheema ,&nbsp;Faisal Khosa","doi":"10.1016/j.clinimag.2024.110370","DOIUrl":"10.1016/j.clinimag.2024.110370","url":null,"abstract":"<div><h3>Purpose</h3><div>The time from article submission to publication in peer-reviewed scientific journals is variable and can be prolonged, which slows the dissemination of research and can influence the academic progress of authors. This study evaluated the publication times for articles in radiology journals, in particular the relationship between turnaround times and journal impact factors (IFs).</div></div><div><h3>Methods</h3><div>Bibliometric data was obtained from Journal Citation Reports to conduct a comparative analysis of radiology journals against those in other disciplines of clinical medicine using highest IF, median IF, cited half-life, immediacy index, and number of journals. Journals from various radiology subcategories were further examined to assess IF trends over time. The Pearson correlation coefficient was used to identify any statistically significant relationships between IF and other variables.</div></div><div><h3>Results</h3><div>Among 28 medical disciplines, there was a significant positive correlation of 0.63 between the number of journals and the highest journal IF of a given discipline. Among 135 radiology journals categorized into 12 subcategories, there was a similar significant correlation of 0.64. For high-ranking radiology journals, the median time from submission to publication online was 22.7 weeks [IQR = 9.3] and median time from submission to publication in print was 37.9 weeks [IQR = 7.1]. The former time interval showed a positive correlation of 0.58 with journal IF at <em>p</em> &lt; 0.05.</div></div><div><h3>Conclusion</h3><div>There is wide variation in the time from submission to publication in radiology journals. Authors can expect a longer turnaround time when publishing in higher-impact journals.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"118 ","pages":"Article 110370"},"PeriodicalIF":1.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review","authors":"Elinor Laws ,&nbsp;Joanne Palmer ,&nbsp;Joseph Alderman ,&nbsp;Ojasvi Sharma ,&nbsp;Victoria Ngai ,&nbsp;Thomas Salisbury ,&nbsp;Gulmeena Hussain ,&nbsp;Sumiya Ahmed ,&nbsp;Gagandeep Sachdeva ,&nbsp;Sonam Vadera ,&nbsp;Bilal Mateen ,&nbsp;Rubeta Matin ,&nbsp;Stephanie Kuku ,&nbsp;Melanie Calvert ,&nbsp;Jacqui Gath ,&nbsp;Darren Treanor ,&nbsp;Melissa McCradden ,&nbsp;Maxine Mackintosh ,&nbsp;Judy Gichoya ,&nbsp;Hari Trivedi ,&nbsp;Xiaoxuan Liu","doi":"10.1016/j.clinimag.2024.110369","DOIUrl":"10.1016/j.clinimag.2024.110369","url":null,"abstract":"<div><h3>Purpose</h3><div>There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digitised screen film mammography, 2D digital mammography and digital breast tomosynthesis) used in the development of AI technologies and present their characteristics, including their transparency of documentation, content, populations included and accessibility.</div></div><div><h3>Materials and methods</h3><div>MEDLINE and Google Dataset searches identified studies describing AI technology development and referencing breast imaging datasets up to June 2024. The characteristics of each dataset are summarised. In particular, the accompanying documentation was reviewed with a focus on diversity and inclusion of populations represented within each dataset.</div></div><div><h3>Results</h3><div>254 datasets were referenced in the literature search, 190 were privately held, 36 had barriers which prevented access, and 28 were accessible. Most datasets originated from Europe, East Asia and North America. There was poor reporting of individuals' attributes: 32 (12 %) datasets reported race or ethnicity; 76 (30 %) reported female/male categories with only one dataset explicitly defining whether these categories represented sex or gender attributes.</div></div><div><h3>Conclusion</h3><div>Through this review, we demonstrate gaps in the data landscape for mammography, highlighting poor representation globally. To ensure datasets in breast imaging have maximum utility for researchers, their characteristics should be documented and limitations of datasets, such as their representativeness of populations and settings, should inform scientific efforts to translate data-driven insights into technologies and discoveries.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"118 ","pages":"Article 110369"},"PeriodicalIF":1.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complicated appendicitis in the pediatric patient: interventional perspectives","authors":"Ronnie W. AlRamahi ,&nbsp;Andrew Woerner ,&nbsp;Hassan Rizvi ,&nbsp;Eric J. Monroe","doi":"10.1016/j.clinimag.2024.110371","DOIUrl":"10.1016/j.clinimag.2024.110371","url":null,"abstract":"<div><div>This pictorial review provides a comprehensive visual and textual overview of interventional radiology approaches in treating complicated appendicitis and other abdominal abscesses in children. This review discusses the incidence and complications associated with appendicitis in pediatric patients, highlighting the role of percutaneous drainage in managing appendicitis with abscesses. We present common mimics of intra-abdominal abscesses from other diseases such as tubo-ovarian abscesses, inflammatory bowel disease, and lymphomatous bowel involvement, emphasizing imaging pitfalls that can mimic appendiceal abscesses. The review also covers interventional radiology considerations, including antibiotic indications, local anesthesia considerations for children, the comparison between percutaneous and endocavitary approaches, and the roles of fibrinolytics are discussed here. Detailed discussions on catheter selection and insertion techniques, such as Seldinger and trocar, are provided along with post-procedure management strategies. These include drain maintenance, navigating drain associated complications, and determining when to remove the drain. Through high-quality images and concise descriptions, we illustrate procedural intricacies and clinical scenarios encountered in practice, offering a valuable educational resource for clinicians managing pediatric abscesses.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"118 ","pages":"Article 110371"},"PeriodicalIF":1.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748249","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":"Opportunistic screening of osteoporosis by CT scan compared to DXA: A systematic review and meta-analysis","authors":"Seyedeh Zahra Mousavi ,&nbsp;Reza Moshfeghinia ,&nbsp;Hossein Molavi Vardanjani ,&nbsp;Mohammad Reza Sasani","doi":"10.1016/j.clinimag.2024.110372","DOIUrl":"10.1016/j.clinimag.2024.110372","url":null,"abstract":"<div><div>The efficacy of opportunistic osteoporosis screening with computed tomography (CT) scans obtained for other indications has not yet been implemented by the current guidelines. We aimed to compile available evidence on the efficacy of osteoporosis screening with CT scans obtained for other indications compared with dual X-ray absorptiometry (DXA).</div><div>Studies comparing the diagnostic performance of the CT scan with the DXA published before 2023 were retrieved. We conducted a bias assessment using the Newcastle-Ottawa Scale for cross-sectional studies. Correlation coefficients (CC), area under the curve (AUC), sensitivity, and specificity of the CT scans compared with the DXA were meta-analyzed with random effects modeling. 41 studies fulfilled the inclusion/exclusion criteria. The included studies reported weak to very strong CC (0.35 to 0.95) and low to high accuracy for opportunistic osteoporosis screening with CT scans. The meta-analysis showed a moderate pooled CC of 0.59 (95 % CI: 0.53–0.64, P-value&lt;0.001), and a relatively high AUC of 0.81 (95 % CI: 0.78–0.84, P-value&lt;0.001). Subgroup analysis based on age and menopausal status did not show significant between-group differences. Significantly higher accuracy measures were estimated for CT scans of the proximal femur compared to other anatomic regions (CC: 0.70, 95 % CI: 0.57–0.82; AUC: 0.79, 95 % CI: 0.72–0.87), North American cases (CC: 0.66, 95 % CI: 0.52–0.80; AUC: 0.82, 95 % CI: 0.82–0.83), and populations with a higher percentage of women (CC: 0.60, 95 % CI: 0.52–0.69; AUC: 0.86, 95 % CI: 0.83–0.89). We observed a moderate performance of opportunistic osteoporosis screening with CT scans obtained for other indications.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"118 ","pages":"Article 110372"},"PeriodicalIF":1.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748246","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":"Inter-observer and intra-observer agreement of bone reporting and data system (Bone-RADS) in the interpretation of bone tumors on computed tomography","authors":"Zainab Ahmad Ramadan (Zainab A. Ramadan),&nbsp;Amel Helmy Elmorsy (Amel Helmy),&nbsp;Saher Ebrahim Taman (Saher Taman),&nbsp;Fatmaelzahraa Abdelfattah Denewar (FA Denewar)","doi":"10.1016/j.clinimag.2024.110367","DOIUrl":"10.1016/j.clinimag.2024.110367","url":null,"abstract":"<div><h3>Objective</h3><div>To assess inter-observer &amp; intra-observer agreement of the American College of Radiology (ACR) bone reporting and data system (Bone-RADS) in the interpretation of bone tumors on computed tomography (CT).</div></div><div><h3>Methods</h3><div>This retrospective study included 273 bone tumors 184 (67.4 %) benign and 89 (32.6 %) malignant. Two blinded radiologists independently reviewed the CT images to assess the defined CT features of bone lesions and assign a bone-RADS category. A third observer reviewed the CT images twice with one month interval and reported the specified CT features and final bone-RADS category for bone lesions. Inter-observer and intra-observer agreement of bone-RADS were analyzed.</div></div><div><h3>Results</h3><div>There was almost perfect inter-observer agreement between the two reviewers for all defined variables as well as bone-RADS categories (Kappa = 92.2 %). Overall intra-observer agreement was also perfect for all defined CT features and bone RADS categories with higher percentage than inter-observer one (κ = 98.3 %).</div></div><div><h3>Conclusion</h3><div>Bone-RADS is an effective clinical tool for practicing radiologists in the risk assessment and management of bone tumors with perfect agreement among observers. To our knowledge, no studies have been conducted for assessing reliability or validity of the recent ACR Bone-RADS using CT. Hence, this study could serve as a cornerstone for further upcoming studies to assess the reproducibility and validity of the ACR Bone-RADS.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110367"},"PeriodicalIF":1.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721758","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":"A real-world comparison of the diagnostic performances of six different TI-RADS guidelines, including ACR-/Kwak-/K-/EU-/ATA-/C-TIRADS","authors":"Osman Melih Topcuoglu,&nbsp;Betul Uzunoglu ,&nbsp;Tolga Orhan,&nbsp;Ekin Bora Basaran,&nbsp;Ayşegul Gormez,&nbsp;Ozgur Sarica","doi":"10.1016/j.clinimag.2024.110366","DOIUrl":"10.1016/j.clinimag.2024.110366","url":null,"abstract":"<div><h3>Purpose</h3><div>To compare the diagnostic performance of six different currently available guidelines including the American College of Radiology Thyroid Imaging and Reporting Data System (ACR-TIRADS), Kwak-TIRADS, Korean TIRADS (K-TIRADS), European TIRADS (EU-TIRADS), American Thyroid Association (ATA) and Chinese TIRADS (C-TIRADS), in differentiating malignant from benign thyroid nodules (TN).</div></div><div><h3>Materials and methods</h3><div>In this single-center study, between January-2007 and September-2023, ultrasound (US) images of TNs that were pathologically proven either by surgery or by fine needle aspiration biopsy (FNAB), were retrospectively evaluated and categorized according to six different currently available guidelines. Area under curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively) and miss rates for malignancy (MRM) were calculated for each guideline.</div></div><div><h3>Results</h3><div>A total of 829 TNs (<em>n</em> = 234 malignant and <em>n</em> = 595 benign) were included. AUC, sensitivity, specificity, PPV, NPV and accuracy for ACR-TIRADS were 0.786, 99.8 %, 27.1 %, 31.92 %, 99.73 % and 54.6 %, respectively; for Kwak-TIRADS 0.839, 97.8 %, 42.1 %, 36.29 %, 98.11 % and 63.1 %, respectively; for K-TIRADS 0.797, 97.6 %, 41.6 %, 36.01 %, 84.85 % and 62.8, respectively, for EU-TIRADS 0.766, 97.8 %, 35.6 %, 33.89 %, 97.92 % and 59.1 %, respectively, for ATA 0.788, 97.5 %, 49.8 %, 32.86 %, 88.16 % and 64.2 %, respectively and for C-TIRADS 0.842, 0 %, 92.8 %, 54.3 %, 39.53 %, 90.42 %, and 68.8 % respectively. MRM regarding ACR-/Kwak-/K-/EU−/ATA-/C-TIRADS were 2.2 %, 0.5 %, 2.9 %, 2.5 %, 3.3 % and 0.1 %, respectively.</div></div><div><h3>Conclusion</h3><div>Six different currently available TIRADS guidelines can provide effective differentiation of malignant TNs from benign ones with similar diagnostic performances. However; C-TIRADS offered the highest AUC and the lowest MRM than the other guidelines, in this series.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110366"},"PeriodicalIF":1.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701255","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}