European Radiology最新文献

{"title":"Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.","authors":"Yuepeng Li, Junyue Deng, Xuelei Ma, Weimin Li, Zhoufeng Wang","doi":"10.1007/s00330-024-11036-4","DOIUrl":"10.1007/s00330-024-11036-4","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis.</p><p><strong>Methods: </strong>Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability.</p><p><strong>Results: </strong>Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes.</p><p><strong>Conclusions: </strong>Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary.</p><p><strong>Clinical relevance statement: </strong>Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies.</p><p><strong>Research registration unique identifying number (uin): </strong>International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1966-1979"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119336","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":"Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners.","authors":"Yan Wu, Xiaopeng Yang, Mingyue Wang, Yanbang Lian, Ping Hou, Xiangfei Chai, Qiong Dai, Baoxin Qian, Yaojun Jiang, Jianbo Gao","doi":"10.1007/s00330-024-11046-2","DOIUrl":"10.1007/s00330-024-11046-2","url":null,"abstract":"<p><strong>Objectives: </strong>It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or cost.</p><p><strong>Methods: </strong>A deep-learning model developed based on 3312 low-dose chest computed tomography (LDCT) scans (trained with 2337 and tested with 975) achieved a mean dice similarity coefficient of 95.8% for T1-T12, L1, and L2 vertebral body (VB) segmentation on test data. We performed a model evaluation based on 4401 LDCT scans (obtained from scanners of 3 different manufacturers as external validation data). The BMD values of all individuals were extracted from three consecutive VBs: T12 to L2. Line regression and Bland‒Altman analyses were used to evaluate the overall detection performance. Sensitivity and specificity were used to evaluate the diagnostic performance for normal, osteopenia, and osteoporosis patients.</p><p><strong>Results: </strong>Compared with the QCT results as the diagnostic standard, the BMD assessed had a mean error of (- 0.28, 2.37) mg/cm³. Overall, the sensitivity of a normal diagnosis was greater than that of a diagnosis of osteopenia or osteoporosis. For the diagnosis of osteoporosis, the model achieved a sensitivity > 86% and a specificity > 98%.</p><p><strong>Conclusion: </strong>The developed tool is clinically applicable and helpful for the positioning and analysis of VBs, the measurement of BMD, and the screening of osteopenia and osteoporosis.</p><p><strong>Clinical relevance statement: </strong>The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest CT scans and performed well on CT images collected from different scanners.</p><p><strong>Key points: </strong>Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fractures. This system could automatically and opportunistically screen for osteoporosis using low-dose chest CT scans obtained for lung cancer screening. The developed system performed well on CT images collected from different scanners and did not differ with patient age or sex.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2287-2295"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132249","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":"MRI-based assessment of the mylohyoid muscle in oral squamous cell carcinoma, a 7-point scoring method.","authors":"E Radin, A V Marcuzzo, J de Groodt, F Degrassi, L Calderan, V Ramella, G Tirelli, M Ukmar, M A Cova","doi":"10.1007/s00330-024-11016-8","DOIUrl":"10.1007/s00330-024-11016-8","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate preoperative MRI evaluation of the features of the mylohyoid muscle (MM) predictive of its infiltration in oral squamous cell carcinoma (OSCC) treatment planning, defining the most appropriate sequences to study its deep extension into the floor of the mouth (FOM).</p><p><strong>Materials and methods: </strong>We applied a 7-point score to retrospectively evaluate preoperative imaging of patients who underwent surgery for OSCC over 11 years. The results were compared with histopathological findings using Spearman's rank coefficient. Receiver operating characteristic curves were employed to assess the score's ability to predict MM infiltration, determining optimal thresholds for sensitivity, specificity, and predictive values. The Mann-Whitney U-test confirmed that infiltration judgments did not overlap around this threshold. Cohen's K statistical coefficient was used to evaluate the interobserver agreement.</p><p><strong>Results: </strong>Fifty-two patients (mean age 66.4 ± 11.9 years, 36 men) were evaluated. Histopathological examination found MM infiltration in 21% of cases (n = 11), with 90% classified in the highest Score categories. A score > 4 proved to be the best cut-off for predicting the risk of MM infiltration, with a sensitivity of 91% (CI: 0.57-0.99), specificity 61% (CI: 0.45-0.76), PPV 38% (CI: 0.21-0.59), and NPV 96% (CI: 0.78-0.99). At the subsequent single-sequence assessment, the TSE-T2wi had the highest diagnostic accuracy, with sensitivity 90% (CI: 0.57-0.99), specificity 70% (CI: 0.53-0.82), PPV 45% (CI: 0.25-0.67), and NPV 96% (CI: 0.80-0.99).</p><p><strong>Conclusion: </strong>The 7-point score is a promising predictor of safe surgical margins for MM in OSCC treatment, with the particular benefit of T2-weighted sequences.</p><p><strong>Clinical relevance statement: </strong>Our scoring system for tumor infiltration of MM, which is easy to use even for less experienced radiologists, allows for uniformity in radiological language, thereby ensuring crucial preoperative information for the surgeon.</p><p><strong>Key points: </strong>The relationship of the MM to an oral lesion may impact surgical planning. As the score increases, there is a greater incidence of infiltration in the MM. Our score system improves radiologists' reporting for MM involvement by tumor.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2065-2073"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT as an effective tool for quality evaluation of radiomics research.","authors":"Ismail Mese, Burak Kocak","doi":"10.1007/s00330-024-11122-7","DOIUrl":"10.1007/s00330-024-11122-7","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.</p><p><strong>Methods: </strong>Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.</p><p><strong>Results: </strong>In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840-0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756-0.919) and 0.914 (95% CI: 0.855-0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9-3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.</p><p><strong>Conclusion: </strong>ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.</p><p><strong>Key points: </strong>Question Is ChatGPT effective and reliable in evaluating radiomics research quality based on RQS? Findings ChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment. Clinical relevance ChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2030-2042"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461245","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":"Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.","authors":"Damiano Caruso, Domenico De Santis, Giuseppe Tremamunno, Curzio Santangeli, Tiziano Polidori, Giovanna G Bona, Marta Zerunian, Antonella Del Gaudio, Luca Pugliese, Andrea Laghi","doi":"10.1007/s00330-024-11059-x","DOIUrl":"10.1007/s00330-024-11059-x","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients.</p><p><strong>Materials and methods: </strong>From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m² were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups.</p><p><strong>Results: </strong>The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001).</p><p><strong>Conclusion: </strong>DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients.</p><p><strong>Clinical relevance statement: </strong>Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality.</p><p><strong>Key points: </strong>Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. \"Double-low\" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2213-2221"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142282500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"We may be closer to automated Kellgren-Lawrence grading for knee osteoarthritis than we thought.","authors":"Yin Xi, Avneesh Chhabra","doi":"10.1007/s00330-024-11064-0","DOIUrl":"10.1007/s00330-024-11064-0","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2296-2297"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142282503","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":"Contrast-enhanced mammography-guided biopsy: Is it safe to be implemented in clinical practice?","authors":"Linei Augusta Brolini Dellê Urban","doi":"10.1007/s00330-024-11099-3","DOIUrl":"10.1007/s00330-024-11099-3","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2116-2118"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461247","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":"Further insights into the use of contrast-enhanced imaging for breast cancer follow-up: the cons view.","authors":"Matthew G Wallis","doi":"10.1007/s00330-024-11097-5","DOIUrl":"10.1007/s00330-024-11097-5","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2144-2146"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461253","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":"The unquestionable marriage between AI and structured reporting.","authors":"Jacob J Visser","doi":"10.1007/s00330-024-11038-2","DOIUrl":"10.1007/s00330-024-11038-2","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1935-1937"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142079748","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":"Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?","authors":"Tahani Ahmad, Alessandro Guida, Samuel Stewart, Noah Barrett, Xiang Jiang, Michael Vincer, Jehier Afifi","doi":"10.1007/s00330-024-11028-4","DOIUrl":"10.1007/s00330-024-11028-4","url":null,"abstract":"<p><strong>Objectives: </strong>Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timely interpretation of the scans.</p><p><strong>Methods: </strong>A population-based cohort of very preterm infants (22⁰-30⁶ weeks) born between 2004 and 2016 in Nova Scotia, Canada. A set of nine sequential CUS images per infant was retrieved at three specific coronal landmarks at three pre-identified times (first, sixth weeks, and term age). A radiologist manually labeled each image as normal or abnormal. The dataset was split into training/development/test subsets (80:10:10). Different convolutional neural networks were tested, with filtering of the most uncertain prediction. The model's performance was assessed using precision/recall and the receiver operating area under the curve.</p><p><strong>Results: </strong>Sequential CUS retrieved for 538/665 babies (81% of the cohort). Four thousand one hundred eighty images were used to develop and test the model. The model performance was only discrete at the beginning but, through different machine learning strategies was boosted to good levels averaging 0.86 ROC AUC (95% CI: 0.82, 0.90) and 0.87 PR AUC (95% CI: 0.84, 0.90) (model uncertainty estimation filters using normalized entropy threshold = 0.5).</p><p><strong>Conclusion: </strong>This study offers proof of the feasibility of applying DL to CUS. This basic diagnostic model showed good discriminative ability to classify normal versus abnormal CUS. This serves as a CAD and a framework for constructing a prognostic model.</p><p><strong>Clinical relevance statement: </strong>This DL model can serve as a computer-aided detection tool to classify CUS of very preterm babies as either normal or abnormal. This model will also be used as a framework to develop a prognostic model.</p><p><strong>Key points: </strong>Binary computer-aided detection models of CUS are applicable for classifying ultrasound images in very preterm babies. This model acts as a step towards developing a model for predicting neurodevelopmental outcomes in very preterm babies. This model serves as a tool for interpretation of CUS in this patient population with a heightened risk of brain injury.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1948-1958"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105754","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}