Journal of Computer Assisted Tomography最新文献

{"title":"Quantitative Evaluation of Acute Pancreatitis Based on Dual-Energy Computed Tomography.","authors":"Yuting Lu, Linxia Wu, Xiaofei Yue, Tao Peng, Ming Yang, Jinhuang Chen, Ping Han","doi":"10.1097/RCT.0000000000001768","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001768","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the value of dual-energy computed tomography (DECT) parameters for the quantitative diagnosis of acute pancreatitis (AP) and classification of its severity.</p><p><strong>Methods: </strong>Patients with AP underwent a plain CT scan and three contrast-enhanced DECT scans. We analyzed the group differences in iodine concentration (IC) and slope of the spectral Hounsfield unit curve (λHU) of the 3-phase enhanced scans (arterial, venous, and delayed phases).</p><p><strong>Results: </strong>The study included 60 AP patients (38 males and 22 females; mean age: 47.43±13.47 y). On the basis of the CT severity index (CTSI), the patients were divided into 2 groups: group A (mild AP, n=26) and group B (moderate/severe AP, n=34). IC and λHU in the arterial and venous phases were all significantly higher in group A than in group B (P<0.001) and could effectively differentiate the 2 groups. The areas under the curve were 0.753 (95% CI: 0.624-0.855), 0.799 (95% CI: 0.676-0.892), 0.774 (95% CI: 0.647-0.872), and 0.842 (95% CI: 0.724-0.923) for IC at arterial and venous phases and λHU at arterial and venous phases, respectively. These parameters decreased with the increase of CTSI, showing significant negative correlations, with r were -0.512 (95% CI: -0.678 to -0.297), -0.492 (95% CI: -0.663 to -0.272), -0.552 (95% CI: -0.707 to -0.346), -0.569 (95% CI: -0.719 to -0.368) for IC at arterial and venous phases and λHU at arterial and venous phases, respectively (P<0.001).</p><p><strong>Conclusions: </strong>DECT imaging can quantitatively analyze AP, and the IC and λHU can be used to distinguish mild and severe cases, adding functional information to the CT morphology to determine the severity and prognosis of the disease.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150481","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":"Deep Learning-accelerated MRI in Body and Chest.","authors":"Naveen Rajamohan, Barun Bagga, Bhavik Bansal, Luke Ginocchio, Amit Gupta, Hersh Chandarana","doi":"10.1097/RCT.0000000000001762","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001762","url":null,"abstract":"<p><p>Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific models focusing on T2, T1, and diffusion-weighted imaging have been developed for the abdomen, pelvis, and chest, with the liver and prostate being the most well-studied organ systems. Variational networks with supervised DL models, including data consistency layers and regularizers, are the most common DLR methods. The common theme for all single-center studies on this subject has been noninferior or superior image quality metrics and lesion conspicuity to conventional sequences despite significant acquisition time reduction. DLR also provides a potential for denoising, artifact reduction, increased resolution, and increased signal-noise ratio (SNR) and contrast-to-noise ratio (CNR) that can be balanced with acceleration benefits depending on the imaged organ system. Some specific challenges faced by DLR include slightly reduced lesion detection, cardiac motion-related signal loss, regional SNR variations, and variabilities in ADC measurements as reported in different organ systems. Continued investigations with large-scale multicenter prospective clinical validation of DLR to document generalizability and demonstrate noninferior diagnostic accuracy with histopathologic correlation are the need of the hour. The creation of vendor-neutral solutions, open data sharing, and diversifying training data sets are also critical to strengthening model robustness.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064127","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":"Quantitative Volumetric Analysis of the Patent Foramen Ovale Tunnel in Coronary Computed Tomography Angiography: Clinical Implications and Diagnostic Significance.","authors":"Leyla Mirzayeva, Nezih Yayli, Sümeyye Nur Budak, Murat Uçar, Hüseyin Koray Kiliç, Gonca Erbaş","doi":"10.1097/RCT.0000000000001766","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001766","url":null,"abstract":"<p><strong>Objectives: </strong>(a) To investigate the relationship between tunnel volume (TV) and morphologic parameters of interatrial septum (IAS) in cases with type 3 and type 4 IAS; (b) To investigate the relationship between TV of the IAS and ischemic gliotic foci in brain MRI.</p><p><strong>Materials and methods: </strong>We retrospectively reviewed the images of 301 cases who underwent CCTA in our center between 2020 and 2022. TV, tunnel length (TL), opening diameter of the right (ODRAE) and left atrium entrance (ODLAE), interatrial groove (IAG) diameter, and free flap length (FFL) were measured. The presence, number, and distribution of ischemic gliotic foci were examined in patients who had undergone brain MRI in the last 5 years before the CCTA. Pearson χ2, the Fisher Exact, Mann-Whitney U, linear regression analysis, Kruskal-Wallis test, and the Spearman correlation tests were used for statistical analysis of the data.</p><p><strong>Results: </strong>A shorter FFL was related to the higher IAS type and increased likelihood of jet flow (P=0.013). The correlation between wide IAG diameter and FFL was statistically significant (P=0.003, r=0.22). The correlation between TV and ODRAE and ODLAE was also statistically significant (P<0.001, r=0.364, P<0.001, r=0.332, respectively). In type 3 and type 4 IAS, TV was associated with an increased number of ischemic gliotic foci (P=0.008) and bilateral distribution (P=0.006) on brain MRI.</p><p><strong>Conclusion: </strong>Measurement of TL, ODRAE, ODLAE, and tunnel diameter in symptomatic cases with type 3 and type 4 IAS is crucial in determining the appropriate treatment approach. By adding the TV to the defined parameters, we thought that this innovation would contribute to invasive and noninvasive treatment management.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003768","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":"Renal Parenchymal Defects Occasionally Observed in Non-Well-Differentiated Perirenal Liposarcomas Unlike in Well-Differentiated Types.","authors":"Yu Nishina, Satoru Morita, Yuko Ogawa, Akihiro Inoue, Yasuhiro Kunihiro, Kazuhiko Yoshida, Toshio Takagi, Goro Honda, Yoji Nagashima, Shuji Sakai","doi":"10.1097/RCT.0000000000001767","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001767","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to clarify the frequency of renal parenchymal defects and deformations in each subtype of perirenal liposarcomas and to compare the differences between well-differentiated and non-well-differentiated types.</p><p><strong>Methods: </strong>Patients with perirenal liposarcomas seen between July 2004 and June 2024 were included. Two radiologists blinded to the subtypes retrospectively evaluated CT or MR images for renal parenchymal defects and deformations. Frequencies of these findings were compared between well-differentiated versus non-well-differentiated types using the Fisher test.</p><p><strong>Results: </strong>Forty-two patients (mean age: 66.3±11.5 y; 15 men) with perirenal liposarcomas were included. Renal parenchymal defects and deformations were observed in 0 (0%) and 1 (7.7%) of 13 well-differentiated, 5 (29.4%) and 6 (35.3%) of 17 dedifferentiated, 3 (37.5%) and 0 (0%) of 8 myxoid, and 1 (25.0%) and 1 (25.0%) of 4 pleomorphic types, respectively. Non-well-differentiated liposarcomas had higher frequencies of renal parenchymal defects and deformations compared with well-differentiated liposarcomas [9 of 29 (31.0%) vs. 0 of 13 (0%), P=0.038 and 7 of 29 (24.1%) vs. 1 of 13 (7.7%), P=0.398].</p><p><strong>Conclusion: </strong>Renal parenchymal defects can be occasionally observed (31.0%) in non-well-differentiated perirenal liposarcomas unlike well-differentiated liposarcomas.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020307","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":"An Integrated Model Combined Conventional Radiomics and Deep Learning Features to Predict Early Recurrence of Hepatocellular Carcinoma Eligible for Curative Ablation: A Multicenter Cohort Study.","authors":"Yong-Hai Li, Gui-Xiang Qian, Yu Zhu, Xue-di Lei, Lei Tang, Xiang-Yi Bu, Ming-Tong Wei, Wei-Dong Jia","doi":"10.1097/RCT.0000000000001764","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001764","url":null,"abstract":"<p><strong>Objective: </strong>Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.</p><p><strong>Methods: </strong>We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).</p><p><strong>Results: </strong>The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.</p><p><strong>Conclusions: </strong>The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039410","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":"Effect of New Generation Snapshot Freeze Combined With Deep Learning Image Reconstruction on Image Quality of Coronary Artery Calcifications and Their Quantification.","authors":"Yongjun Jia, Bingying Zhai, Haifeng Duan, Chuangbo Yang, Jian-Ying Li, Nan Yu","doi":"10.1097/RCT.0000000000001765","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001765","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the effectiveness of the new-generation snapshot freeze (SSF2) algorithm combined with Deep Learning Image Reconstruction (DLIR) in improving the image quality of coronary artery calcifications (CAC) and their quantification.</p><p><strong>Methods: </strong>Coronary artery calcification score (CACS) scans were performed on 69 patients using ECG-triggered noncontrast CT. Four groups of images were reconstructed with SSF2 or without (STD), combined with ASIR-V (Adaptive Statistical Iterative Reconstruction-V) and DLIR: STDASIR-V, STDDLIR, SSF2ASIR-V, and SSF2DLIR. CAC image quality was compared, and inter-observer consistency was evaluated among reconstruction groups. CACS, including the Agatston score (AS), volume score (VS), mass score (MS), and the risk stratification based on AS among groups, were compared.</p><p><strong>Results: </strong>The consistencies of the inter-observer image quality scores were excellent or good (kappa=0.705 to 0.837). SSF2ASIR-V and SSF2DLIR had significantly higher scores than STDASIR-V and STDDLIR in reducing motion artifacts of calcified plaques (P<0.05), while no significant differences between SSF2ASIR-V and SSF2DLIR, or between STDASIR-V and STDDLIR (P>0.05). There was no significant difference in CT values of vessels, subcutaneous fat, and muscle in CAC images, but the noises of SSF2ASIR-V and STDASIR-V images were significantly higher than those of SSF2DLIR and STDDLIR images (P>0.05). STDASIR-V had the highest CACS values, while SSF2DLIR had the lowest. Using AS in STDASIR-V as the reference, 9 patients (13.04%) in SSF2DLIR and 7 patients (10.14%) in SSF2ASIR-V had a risk stratification reduced, while no change in STDDLIR.</p><p><strong>Conclusions: </strong>SSF2 and DLIR significantly reduce motion artifacts and image noise in non-contrast CACS CT, respectively. SSF2 reduces CACS values and risk stratification.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009437","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":"T1WI Radiomics Analysis of Anterior Scalene Muscle: A Preliminary Application in Neurogenic Thoracic Outlet Syndrome.","authors":"Meng Sun, Le Fang, Peiyun Tang, Fangruyue Wang, Ling Jiang, Tianwei Wang","doi":"10.1097/RCT.0000000000001701","DOIUrl":"10.1097/RCT.0000000000001701","url":null,"abstract":"<p><strong>Aim: </strong>This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS).</p><p><strong>Materials and methods: </strong>Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC).</p><p><strong>Results: </strong>Totally, 267 radiomics features were extracted, of which 57 showed significant differences ( P  ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706.</p><p><strong>Conclusions: </strong>NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"486-492"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunistic Quantitative Computed Tomography Assessing Bone Mineral Density in Patients With Laparoscopic Roux-En-Y-Gastric Bypass Metabolic Surgery Throughout a 5-Year Observation Window.","authors":"Mark-Stefan Noser, Daniel T Boll, Ioannis I Lazaridis, Tarik Delko, Thomas Koestler, Urs Zingg, Silke Potthast","doi":"10.1097/RCT.0000000000001705","DOIUrl":"10.1097/RCT.0000000000001705","url":null,"abstract":"<p><strong>Background: </strong>Bariatric surgery is associated with decreasing bone mineral density (BMD).</p><p><strong>Objective: </strong>To assess the long-term vertebral BMD, measured by opportunistic quantitative CT (QCT), and body mass index (BMI) in patients undergoing proximal laparoscopic Roux-en-Y surgery (LRYGB).</p><p><strong>Methods: </strong>In 62 patients undergoing LRYGB, opportunistic QCT measurements were performed extracting BMD and BMI on day 1 and years 1, 3, and 5 postoperatively.Primarily, one-way analyses of variance were performed on dependent variables BMI and BMD, with imaging interval defined as an independent factor. Student-Newman-Keuls tests performed pairwise comparisons of imaging interval permutations for BMI/BMD.Secondarily, analyses of covariance were used on dependent variables BMI and BMD, with imaging interval as an independent factor and gender/age as well as BMD/BMI, respectively, as covariates.</p><p><strong>Results: </strong>A total of 227 opportunistic QCT measurements in 62 patients were performed without the need of a phantom or extra software.The BMD decreased substantially and continuously during 1-, 3-, and 5-year follow-up observations, reaching statistical significance in pairwise comparisons for 3- and 5-year follow-up visits compared to initial BMD values as well as the 5-year follow-up visit compared to the 1-year BMD values, P  < 0.001. Age and BMI were significant covariates, P  < 0.001.The BMI decreased within 1 year and stayed constant until a slight increase at 5 years was observed. Statistical significance in pairwise comparisons for first-year and 3- and 5-year follow-up visits was reached compared to initial BMI values, P  < 0.001. For the BMI assessment, none of the covariates reached statistical significance.</p><p><strong>Conclusion: </strong>Opportunistic QCT is suited for the calculation and follow-up of BMD. There was a continuous decrease of BMD after LRYGB over 5 years post-surgery, whereas BMI decreased in the first year and stayed constant thereafter. Older patients with lower BMI seem particularly prone to an accelerated BMD loss.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"385-390"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780220","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":"Incremental Value of Pericoronary Adipose Tissue Radiomics Models in Identifying Vulnerable Plaques.","authors":"Jinke Zhu, Xiucong Zhu, Sangying Lv, Danling Guo, Huaifeng Li, Zhenhua Zhao","doi":"10.1097/RCT.0000000000001704","DOIUrl":"10.1097/RCT.0000000000001704","url":null,"abstract":"<p><strong>Objective: </strong>Inflammatory characteristics in pericoronary adipose tissue (PCAT) may enhance the diagnostic capability of radiomics techniques for identifying vulnerable plaques. This study aimed to evaluate the incremental value of PCAT radiomics scores in identifying vulnerable plaques defined by intravascular ultrasound imaging (IVUS).</p><p><strong>Methods: </strong>In this retrospective study, a PCAT radiomics model was established and validated using IVUS as the reference standard. The dataset consisted of patients with coronary artery disease who underwent both coronary computed tomography angiography and IVUS examinations at a tertiary hospital between March 2023 and January 2024. The dataset was randomly assigned to the training and validation sets in a 7:3 ratio. The diagnostic performance of various models was evaluated on both sets using the area under the curve (AUC).</p><p><strong>Results: </strong>From 88 lesions in 79 patients, we selected 9 radiomics features (5 texture features, 1 shape feature, 1 gray matrix feature, and 2 first-order features) from the training cohort (n = 61) to build the PCAT model. The PCAT radiomics model demonstrated moderate to high AUCs (0.847 and 0.819) in both the training and test cohorts. Furthermore, the AUC of the PCAT radiomics model was significantly higher than that of the fat attenuation index model (0.847 vs 0.659, P  < 0.05). The combined model had a higher AUC than the clinical model (0.925 vs 0.714, P  < 0.01).</p><p><strong>Conclusions: </strong>The PCAT radiomics signature of coronary CT angiography enabled the detection of vulnerable plaques defined by IVUS.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"422-430"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894878","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":"Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma.","authors":"Yong Zhu, Jiao Chen, Wenjing Cui, Can Cui, Hailin Jin, Jianhua Wang, Zhongqiu Wang","doi":"10.1097/RCT.0000000000001686","DOIUrl":"10.1097/RCT.0000000000001686","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models.</p><p><strong>Materials and methods: </strong>Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36).</p><p><strong>Results: </strong>Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI.</p><p><strong>Conclusions: </strong>CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 3","pages":"358-366"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002639","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}