European Journal of Radiology最新文献

{"title":"A “novel” MRI sequence for improving conspicuity and detection of hemorrhagic foci in pelvic endometriosis: Technical note","authors":"Leandro Accardo de Mattos ,&nbsp;Ulysses S. Torres ,&nbsp;Maria Concepción García Otaduy ,&nbsp;Roberto Blasbalg ,&nbsp;Giuseppe D’Ippolito","doi":"10.1016/j.ejrad.2025.112007","DOIUrl":"10.1016/j.ejrad.2025.112007","url":null,"abstract":"<div><div>There is a growing need to develop new MRI sequences to identify and characterize hemorrhagic foci within endometriosis lesions. These foci are pivotal, as they represent a significant component of the disease’s pathophysiology and have been associated with increased inflammation and angiogenesis. However, their detection within a dense, mixed background of fibrotic tissue is challenging using conventional T1W sequences, even with fat suppression. In this technical report, we propose a T1W 3D-FSE sequence specifically optimized to enhance the detection of hemorrhagic foci in endometriosis. Future clinical validation holds promise for increasing MRI accuracy, ultimately impacting patient management, outcomes, and quality of life.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112007"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of microsatellite-stable/epithelial-to-mesenchymal transition molecular subtype gastric cancer using CT radiomics and clinicopathologic factors","authors":"Chae Young Lim ,&nbsp;Dong Ik Cha ,&nbsp;Woo Kyoung Jeong ,&nbsp;Yoon young Cho ,&nbsp;Sungjun Hong ,&nbsp;Sungsoo Hong ,&nbsp;Kyunga Kim ,&nbsp;Jae-Hun Kim","doi":"10.1016/j.ejrad.2025.111990","DOIUrl":"10.1016/j.ejrad.2025.111990","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop a predictive model for the microsatellite-stable (MSS)/epithelial-to-mesenchymal transition (EMT) subtype of gastric cancer (GC) using computed tomography (CT) radiomics and clinicopathological factors.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study included 418 patients with GC who underwent primary resection and transcriptome analysis with microarray between October 1995 and May 2008. Using preoperative CT images, radiomic features from the volume of interest in the portal venous phase images were extracted. The patient data were randomly divided into training (70%) and testing (30%) datasets. Optimal radiomics features were selected through a thorough feature-selection process. The final radiomic and clinicopathological factors were selected using a stepwise variable selection method. The area under the curve (AUC) was calculated to evaluate performance.</div></div><div><h3>Results</h3><div>Seventy patients had EMT subtype GC, and 348 patients had non-EMT subtype based on transcriptome analysis. There were 276 men (66.0 %), with a median age of 59 years (interquartile range: 50–67). Eleven radiomic features were selected for the prediction model using the combined variance inflation factor (VIF) and least absolute shrinkage and selection operator (LASSO) method. A CT radiomics-based prediction model was constructed using logistic regression with AUCs of 0.824 and 0.736 for training and testing, respectively. When clinicopathological factors such as age, tumor size, signet ring cell histology, and Lauren classification were combined, the AUCs of the models increased to 0.849 and 0.840 for training and testing, respectively (p &lt; 0.001 for testing).</div></div><div><h3>Conclusion</h3><div>A prediction model using CT radiomics and clinicopathological factors showed good performance in predicting the EMT subtype of GC.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 111990"},"PeriodicalIF":3.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Segmentation of Cardiovascular Structures on Chest CT Data Sets: An Update of the TotalSegmentator","authors":"Daniel Hinck ,&nbsp;Martin Segeroth ,&nbsp;Jules Miazza ,&nbsp;Denis Berdajs ,&nbsp;Jens Bremerich ,&nbsp;Jakob Wasserthal ,&nbsp;Maurice Pradella","doi":"10.1016/j.ejrad.2025.112006","DOIUrl":"10.1016/j.ejrad.2025.112006","url":null,"abstract":"<div><h3>Introduction</h3><div>Quantitative analysis is an important factor in radiological routine. Recently the TotalSegmentator was released, a free-to-use segmentation tool with over 104 structures included. Our aim was to add missing and enhance previously included cardiovascular (CV) structures to potentially help find new insights into diseases such as aortic aneurysms in future studies.</div><div>The TotalSegmentator data set with 1613 CT scans (mean age 63.6 ± 15.9 (SD); 675 female), was used. CT scans were selected from clinical routine including various protocols and pathologies. The data set was split in training (1472), validation (57) and testing (84). Segmentations were performed in dedicated imaging software using an iterative approach for training to reduce segmentation workload. Eleven structures were added, and segmentations of six structures were enhanced. The Dice similarity score (DICE) and the Normalized surface distance (NSD) were calculated on an internal and external data set. The external validation was performed on the Dongyang data set. The Mann Whitney <em>U</em> test was performed to evaluate the performance increase on the previously included structures.</div></div><div><h3>Results</h3><div>Median DICE [IQR] and NSD [IQR] were 0.967 [0.020] and 1.000 [0.000], respectively. DICE (p &lt; 0.001) and NSD (p &lt; 0.001) significantly increased for 5/6 structures. On evaluation using the external data set, DICE and NSD were 0.970 [0.020] and 1.000 [0.000].</div></div><div><h3>Conclusion</h3><div>Accurate segmentations and enhanced segmentations of previously included CV structures were successfully implemented. This suggests further usage in research studies while still running on conventional computers with or without a dedicated graphics processing unit.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112006"},"PeriodicalIF":3.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is the predictive value of pretreatment MRI characteristics for achieving a complete response after total neoadjuvant treatment in locally advanced rectal cancer?","authors":"Duygu Karahacioglu ,&nbsp;Hande Ozen Atalay ,&nbsp;Rohat Esmer ,&nbsp;Zeynep Unal Kabaoglu ,&nbsp;Sukran Senyurek ,&nbsp;Ibrahim Halil Ozata ,&nbsp;Orhun Çig Taskin ,&nbsp;Burcu Saka ,&nbsp;Fatih Selcukbiricik ,&nbsp;Ugur Selek ,&nbsp;Ahmet Rencuzogullari ,&nbsp;Dursun Bugra ,&nbsp;Emre Balik ,&nbsp;Bengi Gurses","doi":"10.1016/j.ejrad.2025.112005","DOIUrl":"10.1016/j.ejrad.2025.112005","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate the value of pretreatment magnetic resonance imaging (MRI) features in predicting a complete response to total neoadjuvant treatment (TNT) in locally advanced rectal cancer (LARC).</div></div><div><h3>Methods</h3><div>The data of patients who received TNT were analyzed retrospectively. MRI features, including T stage, morphology, length, and volume; the presence of MR-detected extramural venous invasion (mrEMVI), the number of mrEMVI, and the diameter of the largest invaded vein; main vein mrEMVI; presence of MR-detected tumor deposits (mrTDs), the number of mrTDs, and the size of the largest mrTD; MR-detected lymph node status (mrLN); tumor distance from the anal verge; mesorectal fascia involvement (mrMRF + ); and mean apparent diffusion coefficient (ADC) values were recorded. Patients were classified as complete (CRs) or noncomplete responders (non-CRs) according to the pathological/clinical outcomes. For patients managed nonoperatively, a sustained clinical complete response for &gt; 2 years was deemed a surrogate endpoint for complete response. The MRI parameters were categorized into three distinct groups: baseline, advanced, and quantitative features, and were analyzed using multivariable stepwise logistic regression. The ability to predict complete response was evaluated by comparing different combinations of MRI parameters, and performance on an “independent” dataset was estimated using bootstrapped leave-one-out cross-validation (LOOCV).</div></div><div><h3>Results</h3><div>The data of 84 patients were evaluated (CRs, n = 44; non-CRs, n = 40). The optimal model, which included baseline and quantitative MRI features, achieved an area under the curve of 0.837 for predicting complete response. Selected predictors were T stage and ADC mean value. Advanced MRI features did not improve the performance of the model.</div></div><div><h3>Conclusion</h3><div>A multivariable model combining T stage and the ADC mean value can help identify LARC patients who are likely to a achieve complete response before the initiation of TNT.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112005"},"PeriodicalIF":3.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists","authors":"L. Cereser ,&nbsp;A. Borghesi ,&nbsp;M. De Martino ,&nbsp;T. Nadarevic ,&nbsp;C. Cicciò ,&nbsp;G. Agati ,&nbsp;P. Ciolli ,&nbsp;V. Collini ,&nbsp;V. Patruno ,&nbsp;M. Isola ,&nbsp;M. Imazio ,&nbsp;C. Zuiani ,&nbsp;V. Della Mea ,&nbsp;R. Girometti","doi":"10.1016/j.ejrad.2025.111998","DOIUrl":"10.1016/j.ejrad.2025.111998","url":null,"abstract":"<div><h3>Purpose</h3><div>Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the non-invasive PH assessment. This study aimed to develop a machine learning (ML)-based educational resource for classifying PH cases via CECT according to ESC/ERS groups.</div></div><div><h3>Methods</h3><div>We retrospectively included 172 PH patients who underwent CECT at two University Hospitals (Udine and Brescia). Three chest-devoted radiologists independently reviewed the CECTs, reporting on 13 features, including lung conditions, heart abnormalities, chronic thromboembolism, and mediastinal findings. Readers assigned the features as absent/present except for the left atrium (LA) anteroposterior diameter (measured in millimeters) and classified PH cases I-V with likelihood scores (1–100 %) for each group. The majority decisions for features and average LA diameter were used as ML inputs. The highest average likelihood scores determined group assignments, serving as ground truth. Various ML algorithms were tested using the Weka software and evaluated by accuracy, area under the ROC curve (AUROC), and F1-score.</div></div><div><h3>Results</h3><div>After excluding three group V patients to avoid imbalance, the Naïve-Bayes algorithm showed 0.72 accuracy, 0.84 AUROC, and 0.72 F1-score. Accuracy values for group I-IV were 0.75, 0.78, 0.51, 0.79; AUROC values were 0.78, 0.84, 0.86, 0.87; F1-scores were 0.63, 0.79, 0.61, 0.84, respectively.</div></div><div><h3>Conclusions</h3><div>This study is the first to develop an ML-driven tool for classifying PH via chest CECT. While performance metrics require improvement, including the need for a larger sample size, the resource can potentially train non-dedicated radiologists in PH classification, supporting multidisciplinary reasoning.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 111998"},"PeriodicalIF":3.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrıbutıon of Kaıser score in non-mass enhanced breast lesions","authors":"Ummuhan Ebru Karabulut,&nbsp;Fatma Celik Yabul,&nbsp;Yagmur Basak Polat,&nbsp;Zeynep Donmez,&nbsp;Huseyin Toprak,&nbsp;Alpay Alkan,&nbsp;Seyma Yildiz","doi":"10.1016/j.ejrad.2025.112002","DOIUrl":"10.1016/j.ejrad.2025.112002","url":null,"abstract":"<div><h3>Objective</h3><div>Our study aimed to investigate the effectiveness of Kaiser Score (KS) in diagnosing Non-mass enhanced (NME) lesions and its impact on the inter-reader agreement between experienced and inexperienced readers.</div></div><div><h3>Materials and methods</h3><div>A retrospective analysis was conducted on 189 NME lesions from 182 MRIs. Two readers (an experienced radiologist and a radiology resident) independently evaluated lesions using the KS, blinded to clinical and pathological data. The KS was modified (MKS) by adding 2 points for microcalcifications on mammography and subtracting 4 points for ADC values &gt; 1.4 x 10^-3 mm²/s. Interobserver agreement was assessed with the Intraclass Correlation Coefficient (ICC), and diagnostic performance was evaluated via ROC analysis, with sensitivity and specificity calculated at &gt; 4 and &gt; 5 cut-offs.</div></div><div><h3>Results</h3><div>Interobserver agreement improved with MKS (ICC: 0.763) compared to KS (ICC: 0.667). For the experienced reader, both KS and MKS achieved high sensitivity (&gt;94 %) at a cut-off of &gt; 4. At &gt; 5, specificity improved from 40.5 % to 58.7 % for KS and 39.1 % to 55.8 % for MKS without significantly affecting sensitivity. For the inexperienced reader, MKS improved sensitivity (96.8 %) and specificity (39 %) at &gt; 4. At &gt; 5, specificity increased to 55.8 %, with a non-significant decrease in sensitivity (86.2 %).</div></div><div><h3>Conclusion</h3><div>The Kaiser Score is a quick and systematic tool that enhances diagnostic accuracy and reduces biopsy rates, particularly benefiting inexperienced readers. While higher thresholds improve specificity for experienced readers, they may reduce sensitivity for inexperienced readers, potentially missing malignancies. As a complement to BI-RADS, the Kaiser Score helps standardize evaluations and bridge experience gaps in MRI interpretation.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112002"},"PeriodicalIF":3.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An algorithmic approach to MR characterization of focal liver lesions in adults without cirrhosis","authors":"Shan Su,&nbsp;Neha Yadu,&nbsp;Gaurav Khatri,&nbsp;Hala Khasawneh,&nbsp;Ivan Pedrosa,&nbsp;Takeshi Yokoo","doi":"10.1016/j.ejrad.2025.112001","DOIUrl":"10.1016/j.ejrad.2025.112001","url":null,"abstract":"<div><div>Diagnosing both known and incidental liver lesions in the non-cirrhotic liver on MRI can be challenging. The radiologist can often narrow the diagnosis toward a diagnostic category using various sequences. Using an organized framework to guide the reader’s differential diagnosis can be helpful. We present a sequential approach to the diagnosis of focal liver lesions, by first assessing background liver parenchymal signal intensity, then comparing the T1-weighted signal intensity of the reference organ(s), followed by comparing the T2-weighted signal intensity characteristics of lesion to fluid/spleen, and finally confirming using additional sequences including dynamic contrast-enhanced imaging, hepatobiliary imaging, diffusion weighted imaging, as well as clinical and laboratory testing and additional modalities. Using this stepwise framework can sequentially guide the reader toward a diagnosis.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112001"},"PeriodicalIF":3.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Colon cancer biology and treatment in the era of precision oncology: A primer for Radiologists","authors":"James R. Platt ,&nbsp;Stephanie Pennycook ,&nbsp;Chand E. Muthoo ,&nbsp;Alice C. Westwood ,&nbsp;Russell Frood ,&nbsp;Andrew D. Beggs ,&nbsp;Andrew Scarsbrook ,&nbsp;Jenny F. Seligmann ,&nbsp;Damian J.M. Tolan","doi":"10.1016/j.ejrad.2025.112000","DOIUrl":"10.1016/j.ejrad.2025.112000","url":null,"abstract":"<div><div>In the era of precision oncology, systemic therapies for colon cancer are becoming increasingly biomarker-led, with implications for patients in the neoadjuvant, adjuvant and metastatic settings. As the landscape for colon cancer treatment evolves and becomes more complex, it is important that all members of the multidisciplinary team keep abreast of developments to ensure the most effective care is delivered to patients. As core members of the colorectal multidisciplinary team, Radiologists play a central role throughout the patient journey. This review serves as an educational summary of current and emerging treatment pathways in colon cancer, standards for biomarker testing, mechanisms of action for key drugs, important treatment-related complications, relevant tumour biology that underpins patterns of disease and treatment response, and the specific implications systemic therapies have for cancer imaging and Radiologists. We also highlight the increasing role for radiology in patient stratification and the importance of imaging biomarkers. It is crucial that Radiologists understand the current landscape of colon cancer treatment and emerging strategies on the horizon in clinical trials. Only through engagement across the wider multidisciplinary team will we deliver true personalised medicine for patients with colon cancer.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112000"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and application of AI assisted automatic reconstruction of axial lumbar disc CT images and diagnosis of lumbar disc herniation","authors":"Lidi Wan ,&nbsp;Xiaolian Su ,&nbsp;Zuogang Xiong ,&nbsp;Zhijun Cui ,&nbsp;Guangyu Tang ,&nbsp;Haiying Zhang ,&nbsp;Lin Zhang","doi":"10.1016/j.ejrad.2025.112003","DOIUrl":"10.1016/j.ejrad.2025.112003","url":null,"abstract":"<div><div>Rationale and Objectives.</div><div>To evaluate the value of artificial intelligence (AI) assisted diagnostic system in reconstructing axial lumbar disc CT images and diagnosing lumbar disc herniation.</div></div><div><h3>Materials and Methods</h3><div>440 patients with lumbar disc herniation were included, with 400 cases of spiral data (320 training, 40 validations, and 40 testing) and 40 cases of axial data (testing). V-Net was used to reconstruct the axial lumbar disc images. U-Net was used to segment the herniated discs and perform MSU classification. The Dice coefficient was used to evaluate the accuracy of AI in lumbar vertebras and discs segmentation. The quality of axial CT images reconstructed by AI and radiology technician was compared. The diagnostic accuracy of AI, radiologist, and AI + radiologist for the MSU classification of lumbar disc herniation in spiral and axial data was evaluated.</div></div><div><h3>Results</h3><div>The Dice coefficients of AI for segmenting the sacral, lumbar, and lumbar discs were 0.953, 0.940, and 0.926, respectively. The quality of the axial CT images reconstructed by AI and radiographer had non-significant difference (P＞0.05). In both the spiral and axial data, the accuracy of AI, radiologist, and AI + radiologist in diagnosing the MSU classification was significantly different (P &lt; 0.01). The diagnostic accuracy of the AI system in MSU classification was higher in the spiral data than that of the axial data (P = 0.003).</div></div><div><h3>Conclusion</h3><div>The AI system is feasible and satisfactory for segmentation of lumbar CT image, reconstruction of axial lumbar disc CT images, and diagnosis of lumbar disc herniation.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 112003"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation between tibial tubercle-trochlear groove and 3D shiftactive extension in patellar dislocation: An active extension analysis based on three-dimensional measurements","authors":"Mao Yuan ,&nbsp;Wei Tian ,&nbsp;Bo Sheng ,&nbsp;Jia Li ,&nbsp;Haitao Yang ,&nbsp;Furong Lv ,&nbsp;Yurou Chen","doi":"10.1016/j.ejrad.2025.111999","DOIUrl":"10.1016/j.ejrad.2025.111999","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the correlation between 3D shift_{active extension} (lateral patellar shift in active extension position based on newly established 3D measurement method) and static tibial tubercle-trochlear groove (TT-TG) distance in full knee extension with muscle relaxation.</div></div><div><h3>Methods</h3><div>42 knees of 24 patients with recurrent patellar dislocation were included in the study group and 38 knees of 30 subjects were included in the control group. TT-TG distance, bisect offset index_{muscle relaxation}, bisect offset index_{active extension} and 3D shift_{active extension} were measured to investigate the correlation between patellar lateral shift measurements and TT-TG distance.</div></div><div><h3>Results</h3><div>In the recurrent patellar dislocation group, the correlation between TT-TG distance and 3D shift_{active extension} (r = 0.76 [95 % CI, 0.61 to 0.86]) was significantly higher than correlation between TT-TG distance and bisect offset index_{active extension} (r = 0.61 [95 % CI, 0.32 to 0.79]) and correlation between TT-TG distance and bisect offset index_{muscle relaxation} (r = 0.6 [95 % CI, 0.32 to 0.80]), with p = 0.01 and p = 0.03, respectively.</div></div><div><h3>Conclusion</h3><div>Significantly stronger correlation between TT-TG distance and 3D shift_{active extension} in patients with patellar dislocation was observed in this study. 3D shift_{active extension} could be considered as a preoperative evaluation for tibial tubercle osteotomy to both characterize the patellar lateral shift and estimate tibial tubercle lateralization to simplify the preoperative assessment procedures. The 3D shift_{active extension} obtained under muscle contraction conditions both simulated the functional state of the patellofemoral joint and improved the limitations of the 2D measurements, helping to improve surgical decision-making for functional rehabilitation.</div><div>Level of Evidence: Level III</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"185 ","pages":"Article 111999"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}