European Journal of Radiology Open最新文献

{"title":"Bone lesions of the tibia: Multimodal iconographic review and diagnostic algorithms, Part 1: Diagnostic algorithms, dysplasia and diaphyseal lesions","authors":"Vincent Salmon,&nbsp;Pedro Augusto Gondim Teixeira,&nbsp;Alain Blum","doi":"10.1016/j.ejro.2025.100653","DOIUrl":"10.1016/j.ejro.2025.100653","url":null,"abstract":"<div><div>This article focuses on the analysis of bone lesions of the tibia, addressing the main diagnostic challenges and imaging strategies used to characterize them. It examines the different etiologies of tibial lesions, emphasizing the importance of a systematic approach to distinguishing tumoral from non-tumoral lesions, as well as from bone dysplasia. The article underlines the essential role of imaging, particularly radiography, CT, and MRI, in accurate lesion characterization. It also highlights typical clinical and radiological features that help guide diagnosis and management. The main aim is to provide radiologists with clear guidelines for improving the identification of bony lesions of the tibia. Part 1 of this 2-part article proposes simplified diagnostic algorithms and some illustrations of dysplasia and diaphyseal lesions of the tibia.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100653"},"PeriodicalIF":1.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895788","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":"Deep learning-based acceleration of high-resolution compressed sense MR imaging of the hip","authors":"Alexander W. Marka ,&nbsp;Felix Meurer ,&nbsp;Vanessa Twardy ,&nbsp;Markus Graf ,&nbsp;Saba Ebrahimi Ardjomand ,&nbsp;Kilian Weiss ,&nbsp;Marcus R. Makowski ,&nbsp;Alexandra S. Gersing ,&nbsp;Dimitrios C. Karampinos ,&nbsp;Jan Neumann ,&nbsp;Klaus Woertler ,&nbsp;Ingo J. Banke ,&nbsp;Sarah C. Foreman","doi":"10.1016/j.ejro.2025.100656","DOIUrl":"10.1016/j.ejro.2025.100656","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate a Compressed Sense Artificial Intelligence framework (CSAI) incorporating parallel imaging, compressed sense (CS), and deep learning for high-resolution MRI of the hip, comparing it with standard-resolution CS imaging.</div></div><div><h3>Methods</h3><div>Thirty-two patients with femoroacetabular impingement syndrome underwent 3 T MRI scans. Coronal and sagittal intermediate-weighted TSE sequences with fat saturation were acquired using CS (0.6 ×0.8 mm resolution) and CSAI (0.3 ×0.4 mm resolution) protocols in comparable acquisition times (7:49 vs. 8:07 minutes for both planes). Two readers systematically assessed the depiction of the acetabular and femoral cartilage (in five cartilage zones), labrum, ligamentum capitis femoris, and bone using a five-point Likert scale. Diagnostic confidence and abnormality detection were recorded and analyzed using the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>CSAI significantly improved the cartilage depiction across most cartilage zones compared to CS. Overall Likert scores were 4.0 ± 0.2 (CS) vs 4.2 ± 0.6 (CSAI) for reader 1 and 4.0 ± 0.2 (CS) vs 4.3 ± 0.6 (CSAI) for reader 2 (p ≤ 0.001). Diagnostic confidence increased from 3.5 ± 0.7 and 3.9 ± 0.6 (CS) to 4.0 ± 0.6 and 4.1 ± 0.7 (CSAI) for readers 1 and 2, respectively (p ≤ 0.001). More cartilage lesions were detected with CSAI, with significant improvements in diagnostic confidence in certain cartilage zones such as femoral zone C and D for both readers. Labrum and ligamentum capitis femoris depiction remained similar, while bone depiction was rated lower. No abnormalities detected in CS were missed in CSAI.</div></div><div><h3>Conclusion</h3><div>CSAI provides high-resolution hip MR images with enhanced cartilage depiction without extending acquisition times, potentially enabling more precise hip cartilage assessment.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100656"},"PeriodicalIF":1.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898530","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":"Bone lesions of the tibia: Multimodal iconographic review and diagnostic algorithms, Part 2: Metaphyseal and epiphyseal lesions","authors":"Vincent Salmon,&nbsp;Pedro Augusto Gondim Teixeira,&nbsp;Alain Blum","doi":"10.1016/j.ejro.2025.100654","DOIUrl":"10.1016/j.ejro.2025.100654","url":null,"abstract":"<div><div>This article focuses on the analysis of bone lesions of the tibia, addressing the main diagnostic challenges and imaging strategies used to characterize them. It examines the different etiologies of tibial lesions, emphasizing the importance of a systematic approach to distinguishing tumoral from non-tumoral lesions, as well as from bone dysplasia. The article underlines the essential role of imaging, particularly radiography, CT, and MRI, in accurate lesion characterization. It also highlights typical clinical and radiological features that help guide diagnosis and management. The main aim is to provide radiologists with clear guidelines for improving the identification of bony lesions of the tibia. Part 2 of this 2-part article proposes some illustrations of metaphyseal and epiphyseal lesions of the tibia.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100654"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895905","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":"Evaluation of large language models in generating pulmonary nodule follow-up recommendations","authors":"Junzhe Wen ,&nbsp;Wanyue Huang ,&nbsp;Huzheng Yan ,&nbsp;Jie Sun ,&nbsp;Mengshi Dong ,&nbsp;Chao Li ,&nbsp;Jie Qin","doi":"10.1016/j.ejro.2025.100655","DOIUrl":"10.1016/j.ejro.2025.100655","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To evaluate the performance of large language models (LLMs) in generating clinically follow-up recommendations for pulmonary nodules by leveraging radiological report findings and management guidelines.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included CT follow-up reports of pulmonary nodules documented by senior radiologists from September 1st, 2023, to April 30th, 2024. Sixty reports were collected for prompting engineering additionally, based on few-shot learning and the Chain of Thought methodology. Radiological findings of pulmonary nodules, along with finally prompt, were input into GPT-4o-mini or ERNIE-4.0-Turbo-8K to generate follow-up recommendations. The AI-generated recommendations were evaluated against radiologist-defined guideline-based standards through binary classification, assessing nodule risk classifications, follow-up intervals, and harmfulness. Performance metrics included sensitivity, specificity, positive/negative predictive values, and F1 score.</div></div><div><h3>Results</h3><div>On 1009 reports from 996 patients (median age, 50.0 years, IQR, 39.0–60.0 years; 511 male patients), ERNIE-4.0-Turbo-8K and GPT-4o-mini demonstrated comparable performance in both accuracy of follow-up recommendations (94.6 % vs 92.8 %, P = 0.07) and harmfulness rates (2.9 % vs 3.5 %, P = 0.48). In nodules classification, ERNIE-4.0-Turbo-8K and GPT-4o-mini performed similarly with accuracy rates of 99.8 % vs 99.9 % sensitivity of 96.9 % vs 100.0 %, specificity of 99.9 % vs 99.9 %, positive predictive value of 96.9 % vs 96.9 %, negative predictive value of 100.0 % vs 99.9 %, f1-score of 96.9 % vs 98.4 %, respectively.</div></div><div><h3>Conclusion</h3><div>LLMs show promise in providing guideline-based follow-up recommendations for pulmonary nodules, but require rigorous validation and supervision to mitigate potential clinical risks. This study offers insights into their potential role in automated radiological decision support.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100655"},"PeriodicalIF":1.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886442","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":"Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma","authors":"Liyan Li ,&nbsp;Xueying Wang ,&nbsp;Zeming Tan ,&nbsp;Yipu Mao ,&nbsp;Deyou Huang ,&nbsp;Xiaoping Yi ,&nbsp;Muliang Jiang ,&nbsp;Bihong T. Chen","doi":"10.1016/j.ejro.2025.100650","DOIUrl":"10.1016/j.ejro.2025.100650","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE).</div></div><div><h3>Methods</h3><div>The study included 114 patients with pathology-proven IEE, of whom 80 were randomly assigned to a training group and 34 to a validation group. Preoperative brain MRI images were assessed with the Visually AcceSAble Rembrandt Images (VASARI) feature set. Clinical variables were assessed including age, gender, KPS, pathological grade of the tumor and blood test data such as eosinophil, blood urea nitrogen and serum creatinine. Multivariate Cox proportional hazards regression analysis was performed to select the independent prognostic factors for DFS and OS. Three prediction models were built with clinical variables, MRI-VASARI features, and combined clinical and MRI-VASARI data, respectively. The predictive power of survival models was assessed using c-index and calibration curve.</div></div><div><h3>Results</h3><div>Clinical variables such as eosinophil, blood urea nitrogen and serum creatinine, and MRI-VASARI feature for definition of the non-enhancing margin (F13) were significantly correlated with the prognosis of DFS. Blood urea nitrogen, D-dimer, tumor location (F1), eloquent brain (F3), and T1/FLAIR ratio (F10) were independent predictors of OS. Based on these factors, prediction models were constructed. The concordance indices of the three survival models for OS were 0.732, 0.729, and 0.768, respectively. For DFS, the concordance indices were respectively 0.694, 0.576, and 0.714.</div></div><div><h3>Conclusion</h3><div>Predictive modelling combining both clinical and MRI-VASARI features is robust and may assist in the assessment of prognosis in patients with IEE.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100650"},"PeriodicalIF":1.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value","authors":"Takeharu Kiso ,&nbsp;Yukinori Okada ,&nbsp;Satoru Kawata ,&nbsp;Kouta Shichiji ,&nbsp;Eiichiro Okumura ,&nbsp;Noritaka Hatsumi ,&nbsp;Ryohei Matsuura ,&nbsp;Masaki Kaminaga ,&nbsp;Hikaru Kuwano ,&nbsp;Erika Okumura","doi":"10.1016/j.ejro.2025.100649","DOIUrl":"10.1016/j.ejro.2025.100649","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the usefulness of radiomics features extracted from ultrasonographic images in diagnosing and predicting the severity of knee osteoarthritis (OA).</div></div><div><h3>Methods</h3><div>In this single-center, prospective, observational study, radiomics features were extracted from standing radiographs and ultrasonographic images of knees of patients aged 40–85 years with primary medial OA and without OA. Analysis was conducted using LIFEx software (version 7.2.n), ANOVA, and LASSO regression. The diagnostic accuracy of three different models, including a statistical model incorporating background factors and machine learning models, was evaluated.</div></div><div><h3>Results</h3><div>Among 491 limbs analyzed, 318 were OA and 173 were non-OA cases. The mean age was 72.7 (±8.7) and 62.6 (±11.3) years in the OA and non-OA groups, respectively. The OA group included 81 (25.5 %) men and 237 (74.5 %) women, whereas the non-OA group included 73 men (42.2 %) and 100 (57.8 %) women. A statistical model using the cutoff value of MORPHOLOGICAL_SurfaceToVolumeRatio (IBSI:2PR5) achieved a specificity of 0.98 and sensitivity of 0.47. Machine learning diagnostic models (Model 2) demonstrated areas under the curve (AUCs) of 0.88 (discriminant analysis) and 0.87 (logistic regression), with sensitivities of 0.80 and 0.81 and specificities of 0.82 and 0.80, respectively. For severity prediction, the statistical model using MORPHOLOGICAL_SurfaceToVolumeRatio (IBSI:2PR5) showed sensitivity and specificity values of 0.78 and 0.86, respectively, whereas machine learning models achieved an AUC of 0.92, sensitivity of 0.81, and specificity of 0.85 for severity prediction.</div></div><div><h3>Conclusion</h3><div>The use of radiomics features in diagnosing knee OA shows potential as a supportive tool for enhancing clinicians' decision-making.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100649"},"PeriodicalIF":1.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based risk factors for intensive care unit admissions in acute neck infections","authors":"Jari-Pekka Vierula ,&nbsp;Harri Merisaari ,&nbsp;Jaakko Heikkinen ,&nbsp;Tatu Happonen ,&nbsp;Aapo Sirén ,&nbsp;Jarno Velhonoja ,&nbsp;Heikki Irjala ,&nbsp;Tero Soukka ,&nbsp;Kimmo Mattila ,&nbsp;Mikko Nyman ,&nbsp;Janne Nurminen ,&nbsp;Jussi Hirvonen","doi":"10.1016/j.ejro.2025.100648","DOIUrl":"10.1016/j.ejro.2025.100648","url":null,"abstract":"<div><h3>Objectives</h3><div>We assessed risk factors and developed a score to predict intensive care unit (ICU) admissions using MRI findings and clinical data in acute neck infections.</div></div><div><h3>Methods</h3><div>This retrospective study included patients with MRI-confirmed acute neck infection. Abscess diameters were measured on post-gadolinium T1-weighted Dixon MRI, and specific edema patterns, retropharyngeal (RPE) and mediastinal edema, were assessed on fat-suppressed T2-weighted Dixon MRI. A multivariate logistic regression model identified ICU admission predictors, with risk scores derived from regression coefficients. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis. Machine learning models (random forest, XGBoost, support vector machine, neural networks) were tested.</div></div><div><h3>Results</h3><div>The sample included 535 patients, of whom 373 (70 %) had an abscess, and 62 (12 %) required ICU treatment. Significant predictors for ICU admission were RPE, maximal abscess diameter (≥40 mm), and C-reactive protein (CRP) (≥172 mg/L). The risk score (0−7) (AUC=0.82, 95 % confidence interval [CI] 0.77–0.88) outperformed CRP (AUC=0.73, 95 % CI 0.66–0.80, p = 0.001), maximal abscess diameter (AUC=0.72, 95 % CI 0.64–0.80, p &lt; 0.001), and RPE (AUC=0.71, 95 % CI 0.65–0.77, p &lt; 0.001). The risk score at a cut-off &gt; 3 yielded the following metrics: sensitivity 66 %, specificity 82 %, positive predictive value 33 %, negative predictive value 95 %, accuracy 80 %, and odds ratio 9.0. Discriminative performance was robust in internal (AUC=0.83) and hold-out (AUC=0.81) validations. ML models were not better than regression models.</div></div><div><h3>Conclusions</h3><div>A risk model incorporating RPE, abscess size, and CRP showed moderate accuracy and high negative predictive value for ICU admissions, supporting MRI’s role in acute neck infections.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100648"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of bone-implant interface image quality for in-vivo acetabular cup implants using photon-counting detector CT: Impact of tin pre-filtration","authors":"Ronald Booij ,&nbsp;Pauline de Klerk ,&nbsp;Erik Tesselaar ,&nbsp;Mischa Woisetschläger ,&nbsp;Anne Brandts ,&nbsp;Mariëlle Olsthoorn ,&nbsp;Jakob van Oldenrijk ,&nbsp;Koen Bos ,&nbsp;Jörg Schilcher ,&nbsp;Edwin H.G. Oei","doi":"10.1016/j.ejro.2025.100646","DOIUrl":"10.1016/j.ejro.2025.100646","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the image quality of the bone-implant interface of acetabular cup implants using photon-counting detector (PCD) CT with and without tin pre-filtration in a clinical setting.</div></div><div><h3>Methods and materials</h3><div>Twenty-four patients underwent PCD-CT imaging of their total hip replacement (THR). Twelve patients were scanned using 140 kVp and twelve patients using 140 kVp with tin pre-filtration (Sn140 kVp). All scans were acquired with a collimation of 120 × 0.2 mm. The acquired data was reconstructed with different slice thickness (0.2 mm – 0.6 mm) and kernel (Qr) strengths (56, 76, 89) with and without metal artifact reduction (iMAR). Two observers assessed the image quality of the bone-implant interface for the cup based on four image quality criteria. Bone contrast, contrast-to-noise ratio (CNR) of bone/fat and cortical sharpness was performed as quantitative measures.</div></div><div><h3>Results</h3><div>Image quality was rated highest for 0.2 mm slice thickness and Qr89 kernel across all four criteria for both the 140 kVp and Sn140 kVp by both observers, with a slight preference for the Sn140kVp over the 140 kVp. In all cases and for all image criteria the 0.2 mm/Qr89 was preferred above the Qr76 and Qr56/iMAR for both the 140 kVp and Sn140 kVp by both observers. Quantitative measurements confirmed significantly improved bone contrast as well as cortical sharpness using 0.2 mm/Qr89. Tin pre-filtration did not affect the CNR at 0.2 mm/Qr89 but tended to decrease cortical sharpness.</div></div><div><h3>Conclusions</h3><div>High resolution PCD-CT allows for in-vivo assessment of the bone-implant interface in patients with THR and is preferably acquired with tin pre-filtration.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100646"},"PeriodicalIF":1.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of an automated breast ultrasound system in an academic radiology department: Lesson learned in the first three years","authors":"Elizabet Nikolova ,&nbsp;Julia Weber ,&nbsp;Giulia Zanetti ,&nbsp;Jann Wieler ,&nbsp;Thomas Frauenfelder ,&nbsp;Andreas Boss ,&nbsp;Magda Marcon","doi":"10.1016/j.ejro.2025.100645","DOIUrl":"10.1016/j.ejro.2025.100645","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the diagnostic performance of an ABUS in an academic radiology department over the first three years after its implementation.</div></div><div><h3>Methods</h3><div>In this retrospective study women undergoing ABUS examination for screening and diagnostic purposes between October 2015–2018 were included in case of sufficient follow-up and established diagnosis. Women underwent ABUS + /- mammography in the same day. BI-RADS 1/ 2 cases with cancer diagnosis during follow-up and already visible in the previous exam were considered false negative (FN). BI-RADS 3/4 cases proved benign were considered false positive (FP). FP and number of additional targeted HHUS (addHHUS) were compared over the three years.</div></div><div><h3>Results</h3><div>1248 women (51.2 ± 11.2 years) were included: 956 (77.3 %) underwent ABUS+mammography; 283 (29.3 %) ABUS only. Mean follow-up ± SD was 53.5 ± 17.8 month. Thirty-three malignancies were present in the investigated exams. In 28/ 33 cases (84.8 %), lesions were classified BI-RADS 4 or 5 and one (3.6 %) lesion was only visible in ABUS. 3/33 malignancies (9 %) were classified BI-RADS 3. 2/33 (6 %) were visible in mammography and ABUS but not recognized and classified BI-RADS 2 (FN rate 6.1 %). Retrospectively, both cases had “retraction phenomenon sign” in the coronal images. BI-RADS 3 and BI-RADS 4 without a malignancy were attributed to 172 (13.8 %) and 14 (1.1 %) cases, respectively corresponding to a FP rate of 15.3 %. The number of FP as well as the number of addHHUS significantly reduced over the three years (both p &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>After the implementation of an ABUS FP cases and addHHUS reduce over the time.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100645"},"PeriodicalIF":1.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability of the posteromedial meniscocapsular junction of the knee on MRI: Pitfall to imaging diagnosis of ramp lesions","authors":"Jack Porrino ,&nbsp;Eric Marten ,&nbsp;Michael L. Richardson ,&nbsp;Jay Moran ,&nbsp;Colby Shreve ,&nbsp;Hyojeong Lee ,&nbsp;Andrew Haims","doi":"10.1016/j.ejro.2025.100647","DOIUrl":"10.1016/j.ejro.2025.100647","url":null,"abstract":"<div><h3>Objective</h3><div>A ramp lesion describes injury at the junction of the posterior horn medial meniscus and posteromedial joint capsule occurring with anterior cruciate ligament deficiency. We sought to apply the consensus of the literature’s description of a ramp lesion on MRI (fluid signal interposed between the posterior medial meniscus and adjacent capsule) to a general population to determine how often this “abnormality” is present on routine MRI and help clarify its specificity.</div></div><div><h3>Material and methods</h3><div>100 consecutive MRI knee studies were retrospectively reviewed by 2 radiologists and in binary fashion characterized as either having features of a ramp lesion or normal appearance. If a ramp lesion was present, the lesion was subclassified according to the Thanaut et al. classification. Patient age, laterality, sex, clinical indication, and ancillary findings on MRI were recorded.</div></div><div><h3>Results</h3><div>Thirty-five of 100 (35 %) knees had MRI findings suggesting a ramp lesion with 31/35 (88.6 %) most consistent with a Thanaut et al. type 1. Only 7 of the 35 (20 %) with ramp lesion had ACL insufficiency. Age (p = 0.00044), right laterality (p = 0.019), and female sex (p = 0.029) were statistically associated with this lesion. There was no association with clinical history indicating recent trauma (p = 0.2399).</div></div><div><h3>Conclusion</h3><div>The appearance of the meniscocapsular junction of the posterior horn medial meniscus may be more varied than the literature discussing ramp lesions suggests. Most notably, fluid interposed between the posterior horn medial meniscus and adjacent posteromedial capsule is not uncommon in those undergoing knee MRI and appears to be nonspecific.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100647"},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}