Journal of Computer Assisted Tomography最新文献

{"title":"Commentary: Foreword From the Editor-in-Chief for Guest Section on Low Field Strength MRI.","authors":"Eric P Tamm","doi":"10.1097/RCT.0000000000001875","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001875","url":null,"abstract":"","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838468","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":"Development of an Algorithm to Estimate Fat-Free Mass to Optimize Contrast Injection for Computed Tomography Imaging of the Liver.","authors":"Natalie Heracleous, Hugues Brat, Benoit Dufour, Benoit Rizk, Cyril Thouly, Federica Zanca","doi":"10.1097/RCT.0000000000001877","DOIUrl":"10.1097/RCT.0000000000001877","url":null,"abstract":"<p><strong>Objectives: </strong>Contrast-enhanced computed tomography (CT) is central to liver imaging. Inadequate enhancement can compromise diagnostic accuracy and impact treatment decisions. Fat-free mass (FFM) has emerged as a key predictor of liver enhancement quality, enabling personalized contrast protocols. However, direct FFM measurement is impractical in clinical settings due to equipment costs and time requirements.</p><p><strong>Methods: </strong>We developed a simple machine learning (ML) algorithm to estimate FFM using routinely available patient characteristics, including weight, height, age, and sex. A data set of 689 patients with hepatic CT scans was used to train, validate, and test the algorithm. FFM was benchmarked against measurements from a bioelectrical impedance meter (Biotekna, Italy). Model performance was evaluated through K-fold validation, yielding metrics such as R-squared ( R2 ), mean absolute percentage error (MAPE), and root mean squared error (RMSE). Clinical validation was also performed by measuring the contrast enhancement after the algorithm was implemented using 265 cases.</p><p><strong>Results: </strong>The ML model demonstrated high accuracy in predicting FFM ( R2 =0.915±0.019; MAPE=0.033±0.003). Clinical validation after model implementation in clinical practice showed optimal liver enhancement, corresponding to acceptable image quality, in 89% of cases (235), with a mean enhancement centered at 53 Hounsfield units. The model outperformed existing FFM estimation formulas, demonstrating superior accuracy and generalizability across diverse populations.</p><p><strong>Conclusions: </strong>Our ML-based FFM estimation model facilitates personalized contrast protocols, eliminating the need for expensive equipment and reducing procedural complexity. This approach optimizes liver imaging quality, enhances lesion detection, and supports treatment planning.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772833","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":"Analysis of 18 F-FDG PET/CT Imaging Features of Nodal Diffuse Large B-Cell Lymphoma and Follicular Lymphoma.","authors":"Hao Yu, Chaowei Li, Fei Jin, Wenwen Jiang, Yudong Sui, Lei Zeng, Yanli Wang, Na Fang","doi":"10.1097/RCT.0000000000001872","DOIUrl":"10.1097/RCT.0000000000001872","url":null,"abstract":"<p><strong>Objective: </strong>In this study, we aimed to investigate the value of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) in the differential diagnosis of nodal diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) and to detect early FL transformation.</p><p><strong>Methods: </strong>We retrospectively enrolled 141 and 56 patients with pathologically confirmed DLBCL and FL, respectively, who underwent 18 F-FDG PET/CT before treatment. The most metabolically active lymph nodes in each patient were selected to compare the imaging characteristics (including shape, size, density, adjacent tissue structures, and metabolism) between DLBCL and FL. Logistic regression was performed to identify the independent risk factors for DLBCL, and receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of the different parameters.</p><p><strong>Results: </strong>Compared with DLBCL, FL was more likely to affect the para-iliac (62.5% vs. 32.6%, P <0.05) and inguinal (50.0% vs. 28.4%, P <0.05) regions. Significant differences ( P <0.05) were observed between DLBCL and FL in lesion fusion, necrosis, surrounding fat stranding, adjacent organ involvement, nonround shape, SUV max , SUV peak , SUV mean , SUL max , SUL peak , SUL mean , lesion SUV max /liver SUV max (L/L), lesion SUV max /mediastinum SUV max (L/M), and total lesion glycolysis (TLG). Multivariate logistic regression analysis identified SUV max >12.35, necrosis, and surrounding fat stranding as independent risk factors of DLBCL. The diagnostic model achieved an area under the curve (AUC) of 0.882, with a sensitivity of 69.5% and specificity of 94.6% for diagnosing DLBCL.</p><p><strong>Conclusions: </strong>¹⁸F-FDG PET/CT may provide greater diagnostic value in distinguishing nodal DLBCL from FL and probably aid in the early detection of FL transformation and thereby guide timely clinical intervention, which could improve the prognosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772781","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":"Automated Quantitative Analysis of Enhancing and Peritumoral Cerebral Blood Volume for Differentiating Glioblastoma From Central Nervous System Lymphoma.","authors":"Kazuhiro Murayama, Shohei Harada, Shigeo Ohba, Hiroyuki Maki, Yunosuke Kumazawa, Satomu Hanamatsu, Keigo Tamokami, Keitaro Nishikimi, Saki Ishikawa, Hirotaka Ikeda, Kentaro Tamura, Masanori Inoue","doi":"10.1097/RCT.0000000000001871","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001871","url":null,"abstract":"<p><strong>Objective: </strong>To quantitatively compare cerebral blood volume (CBV) in contrast-enhancing tumor areas and nonenhancing peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintense regions between glioblastoma and central nervous system lymphoma (CNSL), and to assess the incremental diagnostic value of peritumoral CBV beyond enhancing tumor CBV.</p><p><strong>Methods: </strong>The study included 34 patients with histopathologically confirmed glioblastoma (n=22) or CNSL (n=12) who underwent pretreatment magnetic resonance imaging, including FLAIR, dynamic susceptibility contrast perfusion imaging, and contrast-enhanced T1-weighted imaging. Automated regions of interest (ROIs) were defined for enhancement areas (EAs) and nonenhancing peritumoral FLAIR abnormalities (PFAs). Quantitative indices included the median CBV within EAs (CBVEA) and the 95th percentile CBV within PFAs (CBVPFA). Intergroup differences were assessed, and diagnostic performance was evaluated using univariate and multivariate logistic regression models incorporating CBVEA and CBVPFA, receiver operating characteristic (ROC) analysis, and likelihood ratio testing (LRT).</p><p><strong>Results: </strong>Both CBVEA and CBVPFA were significantly higher in the glioblastoma group than in the CNSL group [CBVEA: 4.90 (4.14-5.58) vs. 2.84 (2.04-3.21) mL/100 g, P<0.0001, CBVPFA: 5.67 (3.75-8.08) vs. 4.10 (3.00-5.23) mL/100 g; P=0.029]. CBVEA showed the highest discriminatory performance in univariate analysis, whereas CBVPFA demonstrated a more modest association with tumor type. Although the AUC of the combined model was not significantly different from that of the CBVEA model alone [CBVEA: 0.913 (0.816-1.000) vs. combined model: 0.932 (0.848-1.000), P=0.602], the combined model showed a significant improvement in model fit according to LRT (χ2=4.1, P=0.043).</p><p><strong>Conclusions: </strong>Automated ROI-based analysis demonstrated significant differences in peritumoral CBV between glioblastoma and CNSL, and adding peritumoral CBV to the logistic regression model significantly improved overall model fit for differentiating between these entities beyond enhancing tumor CBV alone.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772844","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":"Percutaneous Lung Biopsies Aided by Artificial Intelligence: A Comparison Between Computer and Physician-Chosen Biopsy Paths.","authors":"Adrienne L Kisting, Meridith A Kisting, J Louis Hinshaw, Allison B Couillard, Giuseppe V Toia, Fred T Lee, Martin G Wagner","doi":"10.1097/RCT.0000000000001868","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001868","url":null,"abstract":"<p><strong>Objective: </strong>To compare Trajectory Recommendation Algorithm for CT-guided Biopsy (TRAX)-generated lung biopsy puncture pathways versus physician-chosen paths.</p><p><strong>Materials and methods: </strong>TRAX is an artificial intelligence (AI)-based algorithm that uses segmentation and physician-chosen logic rules to generate lung biopsy pathways. Once a target lesion is defined by the physician, TRAX generates and ranks ∼20,000 candidate pathways within an axial angle of ±20°. Blinded radiologists retrospectively rated pathways chosen by physicians (n=53) versus TRAX (n=53) from the same patients and setup scans prior to lung biopsies (scale: 1 to 3 safe, 4 to 5 unsafe). The quality and metrics of the pathways were compared.</p><p><strong>Results: </strong>All TRAX and physician-chosen pathways were determined safe by physician reviewers (rating 1 to 3). Ratings were identical in 93/159 (58%) cases; TRAX was superior in 36/159 (23%) cases, and physician paths were superior in 30/159 (19%) (no significant difference between pathways, P=0.61). TRAX pathways were shorter than physician pathways (7.2±2.5 vs. 7.8±2.1 cm, P=0.046). Most TRAX pathways were outside of the axial plane [n=27/53 (50.9%)], mean gantry angle=11.4±6.0°. The majority of physician-generated pathways were in the axial plane [n=43/53 (81.1%)], mean gantry angle=0.9±2.9° (TRAX vs. physician P<0.05 for proportion of paths in the axial plane and mean gantry angle).</p><p><strong>Conclusions: </strong>TRAX appears to be a promising AI tool to assist physicians in selecting needle trajectories for percutaneous CT-guided lung biopsies, particularly those outside the axial plane.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772805","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":"Timing and Energy Optimization of Dual-Energy CT for Brain Metastasis Conspicuity: A Prospective 24-Patient Study.","authors":"Jason M Johnson, Abhi Rashiwala, Apollo Krayyem, Ethan Wang, Hana Farzaneh, Halyna Pokhylevych, Ahmad Amer, Rick Layman, Dawid Schellingerhout, Chirag B Patel","doi":"10.1097/RCT.0000000000001870","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001870","url":null,"abstract":"<p><strong>Background: </strong>Brain metastases are frequently encountered and typically evaluated by MRI, but MRI is not always available or feasible. Dual-energy CT (DECT) with virtual monochromatic reconstructions (VMRs) may offer a rapid, accessible alternative. We aimed to determine the optimal post-contrast timing and VMR energy for visualizing brain metastases using DECT.</p><p><strong>Materials and methods: </strong>This prospective study enrolled 24 patients with known brain metastases. DECT head scans were acquired at 90 seconds, 5 minutes, 10 minutes, and 20 minutes post-contrast using a dual-source scanner. VMR images were generated from 40 to 190 keV. For each time point, we measured lesion and background attenuation across keV, and calculated signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Friedman and Wilcoxon tests were used to compare timing and energy effects.</p><p><strong>Results: </strong>Lesion conspicuity declined after the vascular phase. At 90 seconds, average CNR and SNR were 2.22 ± 0.39 (95% CI: 1.83-2.62) and 8.19 ± 0.67 (7.52-8.85), respectively. These values at 20 minutes measured 1.85 ± 0.33 and 8.34 ± 0.64. CNR peaked consistently at 40 keV, whereas SNR peaked between 70 and 100 keV depending on phase. Statistical tests confirmed significant variation across both time and energy (P < 0.001).</p><p><strong>Conclusions: </strong>Optimal visualization of brain metastases on DECT is achieved at 90 seconds post-contrast using low-to-mid keV reconstructions. Although 40 keV maximized contrast, 70 to 85 keV offered a favorable balance of contrast and noise, supporting its use for interpretation and radiosurgical planning.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772762","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":"Whole-tumor Multiparametric MRI Histogram Analysis for Predicting Intracranial Solitary Fibrous Tumor Grades and Ki-67 Expression.","authors":"Hua Zhang, Xiaoling Li, Dairong Cao, Zhen Xing, Xingfu Wang","doi":"10.1097/RCT.0000000000001873","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001873","url":null,"abstract":"<p><strong>Purpose: </strong>Our aim was to explore the noninvasive prediction of intracranial solitary fibrous tumors (ISFTs) with World Health Organization (WHO) grade and Ki-67 expression level based on semantic and whole-tumor histogram features from multiparametric Magnetic Resonance Imaging (MRI).</p><p><strong>Methods: </strong>We retrospectively evaluated thirty-nine ISFTs with histologically proved WHO grade 1 (n=25), WHO grade 2-3 (n=14), low Ki-67 (n=31), and high Ki-67 (n=8). Clinical data, MR semantic, and whole-tumor histogram features were collected. The values between the two groups were compared with Mann-Whitney U test or Fisher's exact test. Logistic regression analysis, receiver operating characteristic curve, and integrated discrimination improvement (IDI) were applied to identify the diagnostic performance.</p><p><strong>Results: </strong>Hemorrhage and tumor-brain interface showed significant differences between WHO grade 1 and 2-3 groups (all P＜0.05). WHO grade 1 group showed significantly higher CE-T1WI_mean (contrast-enhanced T1-weighted imaging), CE-T1WI_median, and CE-T1WI_P90 values than grade 2-3 group (all P＜0.05). The combined model integrating tumor-brain interface and CE-T1WI_median achieved the highest area under the receiver operating characteristic curves (AUC) of 0.84, which was superior to each single model with improved IDI (IDI=0.15-0.21). Cystic degeneration and midline shift showed significant differences between high and low Ki-67 groups (all P＜0.05). High Ki-67 group was showed lower ADC_mean (apparent diffusion coefficient), ADC_median, ADC_P90, and T2WI_skewness (T2-weighted imaging) values than low Ki-67 group (all P＜0.05). The combined model integrating ADC_median and T2WI_skewness achieved the highest AUC of 0.94 and outperformed each single model with improved IDI (IDI=0.34-0.43).</p><p><strong>Conclusion: </strong>The combination of MR semantic and whole-tumor histogram features can predict ISFT grades and Ki-67 expression with favorable predictive performance.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772771","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 Preliminary Study of a Machine Learning Prediction of Poorly Differentiated Hepatocellular Carcinoma Based on a Comprehensive Parameter Analysis Using Dual-Energy Computed Tomography.","authors":"Atsushi Takamatsu, Norihide Yoneda, Fumihito Toshima, Taichi Kitagawa, Takahiro Komori, Dai Inoue, Azusa Kitao, Kazuto Kozaka, Osamu Matsui, Satoshi Kobayashi","doi":"10.1097/RCT.0000000000001867","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001867","url":null,"abstract":"<p><strong>Objective: </strong>To develop and evaluate the performance of a predictive machine learning model for poorly differentiated hepatocellular carcinoma (p-HCC) using comprehensive quantitative parameters from dual-energy computed tomography (DECT).</p><p><strong>Materials and methods: </strong>We retrospectively analyzed 181 surgically resected, pathologically proven HCCs in 170 patients who had undergone preoperative DECT between April 2019 and November 2025. After propensity-score matching on age, sex, alpha-fetoprotein (AFP), tumor size on CT, and LI-RADS categories, the dataset was divided into a training set including 51 HCCs (17 p-HCCs and 34 non-p-HCCs) from 2019 to 2022 and a testing set including 33 HCCs (11 p-HCCs, 22 non-p-HCCs) from 2023 to 2025. Overall, 3516 DECT parameters were extracted from precontrast images and three contrast-enhanced images [arterial phase (AP), portal venous phase (PVP), and delayed phase (DP)] for each case. These parameters included virtual monochromatic imaging (VMI) CT values, effective atomic numbers (Effective-Z), material density, spectral curve slopes (Slope), and interphase differences (Diff). Clinical data, including etiology of liver disease, serum AFP level, and lesion size, were collected. A machine learning model based on an extra trees classifier was trained, and a Shapley additive explanations (SHAP) analysis was performed for each selected parameter following data preprocessing. Model performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>SHAP analysis revealed that the most highly influential features were delayed phase-related, with the average 40-keV VMI CT value in the delayed phase contributing most strongly. In addition, several Effective-Z-related features and selected material density parameters also showed substantial importance. In the testing set, the model demonstrated an accuracy, sensitivity, specificity, PPV, NPV, and AUC of 0.606, 0.818, 0.500, 0.450, 0.846, and 0.800, respectively.</p><p><strong>Conclusions: </strong>The predictive machine learning model for p-HCC showed acceptable diagnostic performance and, although still preliminary, may contribute to a noninvasive assessment of HCC differentiation grades, guiding clinical decision-making.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690297","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":"Comparison Between Weight-Based Variable-Voltage and Fixed Volume-Voltage Protocols in Cancer Surveillance Computed Tomography.","authors":"Joseph J Ipsen, Kirsten Richards, James H Seow, Alexander Diamant, Alvin Tanaya, Arathy Harikumar, Eric Williams, Angela Kilcoyne, Ashik Amlani, Christopher J Welman","doi":"10.1097/RCT.0000000000001865","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001865","url":null,"abstract":"<p><strong>Objective: </strong>This study assessed whether the weight-based, variable tube voltage (Bayer MEDRAD Centargo SMART) protocol compared with a fixed contrast, fixed tube voltage (referred to as the 'OLD') protocol for cancer patients undergoing CT chest, abdomen, and pelvis (CAP) scans could reduce contrast dose, without a reduction in contrast enhancement.</p><p><strong>Methods: </strong>A total of 149 oncology patients, each underwent 2 surveillance CT CAP scans using both the OLD and SMART protocols, typically with a 3-month interval between scans. Contrast volume was recorded for each protocol as well as the CT attenuation measurement Hounsfield unit (HU) at the ascending aorta, pulmonary trunk, right atrial appendage, middle hepatic vein, main portal vein, and renal cortex, as well as dose-length product (DLP, mGy/cm) and CT Dose Index volume (CTDIvol, mGy).</p><p><strong>Results: </strong>The SMART protocol used less contrast per patient with a median volume of 80 versus 100 mL representing a 20% absolute reduction. Quantitative contrast enhancement measurements demonstrated non-inferior HU values in the SMART protocol at all sites. This was achieved with a 19% DLP and 9% CTDIvol decrease in radiation exposure.</p><p><strong>Conclusions: </strong>The weight-based contrast, variable tube voltage protocol effectively reduces contrast volume without increasing radiation dose and does not reduce contrast enhancement.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690273","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 Meniscal Ramp Lesions According to the Type of Anterior Cruciate Ligament Tear: A Magnetic Resonance Imaging Study.","authors":"Ji N Kim, Hee J Park, Do Y Ahn, Myung S Kim, Seok W Hong","doi":"10.1097/RCT.0000000000001864","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001864","url":null,"abstract":"<p><strong>Purpose: </strong>Meniscal ramp lesions are frequently associated with anterior cruciate ligament (ACL) injuries but may be overlooked during arthroscopy, potentially affecting surgical planning and outcomes. This study aimed to evaluate the prevalence and types of meniscal ramp lesions on magnetic resonance (MR) imaging and to assess the relationship with ACL tear patterns and clinical variables.</p><p><strong>Methods: </strong>This retrospective study included 122 patients who underwent knee MR imaging before arthroscopic ACL reconstruction. ACL tears were classified as both-bundle or selective bundle tears based on arthroscopic findings. Meniscal ramp lesions were classified into 5 subtypes on MR imaging. Associations among meniscal ramp lesion prevalence, lesion subtype, ACL tear pattern, and clinical variables, including body mass index, were analyzed.</p><p><strong>Results: </strong>Meniscal ramp lesions were detected in 74.6% and 77.0% of patients by reader 1 and reader 2, respectively. Meniscal ramp lesions were significantly more frequent in both-bundle ACL tears than in selective bundle tears (84.0% vs. 56.1% for reader 1; 84.0% vs. 63.4% for reader 2; P<0.05). There was no significant difference in ramp lesion prevalence between anteromedial and posterolateral bundle tears or in ramp lesion subtype distribution according to ACL tear pattern.</p><p><strong>Conclusions: </strong>Meniscal ramp lesions are highly prevalent in patients with ACL tears and occur more frequently in both-bundle tears. MR imaging plays a critical role in detecting ramp lesions and may improve preoperative assessment and surgical planning.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147673806","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}