Journal of Computer Assisted Tomography最新文献

{"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":"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}

{"title":"Improving Image Quality and Visualization of Hepatocellular Carcinoma in Arterial Phase Imaging Using Contrast Enhancement-Boost Technique.","authors":"Gayoung Yoon, Jhii-Hyun Ahn, Sang-Hyun Jeon Bs","doi":"10.1097/RCT.0000000000001684","DOIUrl":"10.1097/RCT.0000000000001684","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique.</p><p><strong>Methods: </strong>This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures.</p><p><strong>Results: </strong>The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images ( P  < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images ( P  < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images ( P  < 0.001).</p><p><strong>Conclusions: </strong>The overall image quality and visibility of HCC were improved using the CE-boost technique.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"348-357"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604805","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":"Reconstruction Kernel Optimization for Ultra-High-Resolution Photon-Counting Detector Computed Tomography of the Lung.","authors":"Adrienn Tóth, Jordan H Chamberlin, Carter D Smith, Dhruw Maisuria, Aaron M McGuire, U Joseph Schoepf, Jim O'Doherty, Reginald F Munden, Jeremy Burt, Dhiraj Baruah, Ismail M Kabakus","doi":"10.1097/RCT.0000000000001694","DOIUrl":"10.1097/RCT.0000000000001694","url":null,"abstract":"<p><strong>Background: </strong>The latest generation of computed tomography (CT) systems based on photon-counting detector promises significant improvements in several clinical applications, including chest imaging.</p><p><strong>Purpose: </strong>The aim of the study is to evaluate the image quality of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung using four sharp reconstruction kernels.</p><p><strong>Material and methods: </strong>This retrospective study included 25 patients (11 women and 14 men; median age, 71 years) who underwent unenhanced chest CT from April to May 2023. Images were acquired in UHR mode on a clinical dual-source PCD-CT scanner and reconstructed with four sharp kernels (Bl64, Br76, Br84, Br96). Quantitative image analysis included the measurement of image noise, and the calculation of signal-to-noise ratio, and contrast-to-noise ratio. Two radiologists independently rated the images on a 5-point Likert scale for image sharpness, image noise, overall image quality, and airway details. The 4 image sets were compared pairwise in the statistical analysis.</p><p><strong>Results: </strong>Image noise was lowest for Br76 (74.16 ± 22.05, P  < 0.001). Signal-to-noise ratio was significantly higher in the Br76 images (13.34 ± 3.47), than in the other 3 image sets (all P  < 0.001). The Br76 images demonstrated the highest contrast-to-noise ratio among all reconstructions (1.54 ± 0.86, all P  < 0.001). Subjective image sharpness, image noise, overall image quality, and airway detail were best in the Br76 images (all P  < 0.001 to P  < 0.01, for both readers).</p><p><strong>Conclusions: </strong>The use of the Br76 reconstruction kernel provided the best quantitative and qualitative image quality for UHR PCD-CT of the lungs.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 3","pages":"456-461"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986930","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":"Assessing the Severity of Connective Tissue-Related Interstitial Lung Disease Using Computed Tomography Quantitative Analysis Parameters.","authors":"Ningling Su, Fan Hou, Hongmei Zhu, Jinlian Ma, Feng Liu","doi":"10.1097/RCT.0000000000001693","DOIUrl":"10.1097/RCT.0000000000001693","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;The aims of the study are to predict lung function impairment in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) through computed tomography (CT) quantitative analysis parameters based on CT deep learning model and density threshold method and to assess the severity of the disease in patients with CTD-ILD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We retrospectively collected chest high-resolution CT images and pulmonary function test results from 105 patients with CTD-ILD between January 2021 and December 2023 (patients staged according to the gender-age-physiology [GAP] system), including 46 males and 59 females, with a median age of 64 years. Additionally, we selected 80 healthy controls (HCs) with matched sex and age, who showed no abnormalities in their chest high-resolution CT. Based on our previously developed RDNet analysis model, the proportion of the lung occupied by reticulation, honeycombing, and total interstitial abnormalities in CTD-ILD patients (ILD% = total interstitial abnormal volume/total lung volume) were calculated. Using the Pulmo-3D software with a threshold segmentation method of -260 to -600, the overall interstitial abnormal proportion (AA%) and mean lung density were obtained. The correlations between CT quantitative analysis parameters and pulmonary function indices were evaluated using Spearman or Pearson correlation coefficients. Stepwise multiple linear regression analysis was used to identify the best CT quantitative predictors for different pulmonary function parameters. Independent risk factors for GAP staging were determined using multifactorial logistic regression. The area under the ROC curve (AUC) differentiated between the CTD-ILD groups and HCs, as well as among GAP stages. The Kruskal-Wallis test was used to compare the differences in pulmonary function indices and CT quantitative analysis parameters among CTD-ILD groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 105 CTD-ILD patients (58 in GAP I, 36 in GAP II, and 11 in GAP III), results indicated that AA% distinguished between CTD-ILD patients and HCs with the highest AUC value of 0.974 (95% confidence interval: 0.955-0.993). With a threshold set at 9.7%, a sensitivity of 98.7% and a specificity of 89.5% were observed. Both honeycombing and ILD% showed statistically significant correlations with pulmonary function parameters, with honeycombing displaying the highest correlation coefficient with Composite Physiologic Index (CPI, r = 0.612). Multiple linear regression results indicated honeycombing was the best predictor for both the Dlco% and the CPI. Furthermore, multivariable logistic regression analysis identified honeycombing as an independent risk factor for GAP staging. Honeycombing differentiated between GAP I and GAP II + III with the highest AUC value of 0.729 (95% confidence interval: 0.634-0.811). With a threshold set at 8.0%, a sensitivity of 79.3% and a specificity of 57.4% were obse","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 3","pages":"448-455"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023010","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":"Evaluation of Amide Proton Transfer Imaging Combined With Serum Squamous Cell Carcinoma Antigen for Grading Cervical Cancer.","authors":"Xiao-Yan Zhang, Chen Xu, Xing-Chen Wu, Qian-Qian Qu, Kai Deng","doi":"10.1097/RCT.0000000000001699","DOIUrl":"10.1097/RCT.0000000000001699","url":null,"abstract":"<p><strong>Objective: </strong>The aim of the study is to investigate the efficacy of amide proton transfer-weighted (APT) imaging combined with serum squamous cell carcinoma antigen (SCC-Ag) in grading cervical cancer.</p><p><strong>Methods: </strong>Sixty-three patients with surgically confirmed cervical SCC were enrolled and categorized into 3 groups: highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). The diagnostic efficacies of APT imaging and serum SCC-Ag, alone or in combination, for grading cervical SCC were compared.</p><p><strong>Results: </strong>The APT values measured by the 2 observers were in excellent agreement (intraclass correlation coefficient >0.75). Mean (± standard deviation) APT values for the high, moderate, and poor differentiation groups were 2.542 ± 0.215% (95% confidence interval [CI]: 2.423-2.677), 2.784 ± 0.175% (95% CI: 2.701-2.856), and 3.120 ± 0.221% (95% CI: 2.950-3.250), respectively. APT values for groups G2 and G3 were significantly higher than those for G1 ( P  < 0.05). APT values for identifying cervical SCC in groups G1 and G2, G2 and G3, and G1 and G3, had areas under the receiver operating characteristic curve, sensitivities, and specificities of 0.815 (95% confidence interval [CI]: 0.674-0.914), 82.1%, and 72.2%, 0.882 (95% CI: 0.751-0.959), 70.6%, and 92.7%, and 0.961 (95% CI: 0.835-0.998), 94.1%, and 94.4%, respectively. APT values were significantly and positively correlated with the histological grade of cervical SCC (Spearman's correlation [ rs ] = 0.731, P  < 0.01). Serum SCC-Ag levels for the high, moderate, and poor differentiation groups were 1.60 (0.88-4.63) ng/mL, 4.10 (1.85-6.98) ng/mL, and 26.10 (9.65-70.00) ng/mL, respectively. The differences were statistically significant only between groups G1 and G3 and G2 and G3 ( P  < 0.05), whereas the differences between groups G1 and G2 were not statistically significant ( P  > 0.05). Spearman's analysis revealed a positive correlation between SCC-Ag levels and the histological grade of cervical SCC ( rs  = 0.573, P  < 0.01). The diagnostic efficacy of APT imaging for the histological grading of cervical SCC was better than that of serum SCC-Ag, and the discriminatory efficacy of the combination of the 2 parameters was better than that of either alone.</p><p><strong>Conclusions: </strong>The diagnostic efficacy of APT imaging was better than that of serum SCC-Ag, and the combined diagnostic utility of APT and SCC-Ag was better than that of the individual parameters.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"399-406"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710170","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":"Prediction of Local Tumor Progression After Thermal Ablation of Colorectal Cancer Liver Metastases Based on Magnetic Resonance Imaging Δ-Radiomics.","authors":"Xiucong Zhu, Jinke Zhu, Chenwen Sun, Fandong Zhu, Bing Wu, Jiaying Mao, Zhenhua Zhao","doi":"10.1097/RCT.0000000000001702","DOIUrl":"10.1097/RCT.0000000000001702","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Δ-radiomics and clinical feature-based modeling was employed.</p><p><strong>Materials and methods: </strong>In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Δ-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Δ model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP.</p><p><strong>Results: </strong>LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size ( P  = 0.010), and ΔCEA ( P  = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Δ model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences ( P  = 0.6087).</p><p><strong>Conclusions: </strong>Combined models incorporating clinical data and Δ-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"377-384"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780222","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":"Diagnostic Performance of Imaging Methods in Predicting Lung Cancer Metastases.","authors":"Murat Aşik, Zeynep Nihal Kazci","doi":"10.1097/RCT.0000000000001706","DOIUrl":"10.1097/RCT.0000000000001706","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the possibility of distant organ metastasis using an algorithm developed to evaluate the morphology and localization of lung masses.</p><p><strong>Methods: </strong>Patients diagnosed with lung cancer between 2016 and 2023 were included. The lesion's morphological characteristics, proximity to important structures, and maximum standardized uptake value were recorded. Six common metastatic sites were identified: the contralateral lung, liver, brain, adrenal glands, bone, and other regions. The relationship between the characteristics of the mass and the metastatic location was investigated.</p><p><strong>Results: </strong>A total of 383 patients (260 men, 68%) with malignant lung lesions with a mean ± SD age of 65.50 ± 12.34 years (range: 36-74 years) were included in the study. Among them, 242 were diagnosed with primary lung cancer, and 106 (43.8%) exhibited metastases to other organs with primary lung tumors. Distant organ metastases were most frequently detected in the bones (n = 45, 42.5%) and were more frequent in male patients and lesions adjacent to the ribs and bronchi, those involving mediastinal lymph nodes, irregular contours, and maximum standardized uptake values above 11.15 ± 5.67 (mean ± SD).</p><p><strong>Conclusions: </strong>Evaluating radiological imaging of malignant lesions in patients with lung cancer using an algorithm that considers morphological and neighborhood characteristics can provide predictive information regarding the possibility of metastasis of malignant lung lesions and the metastatic location.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"462-470"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813374","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":"Clinical Impact of Pancreatic and Peripancreatic Hemorrhage Associated With Acute Pancreatitis.","authors":"Benjamin I Richter, Joseph H Weissbrot, Frank R Chung, Tamas A Gonda, Chenchan Huang","doi":"10.1097/RCT.0000000000001683","DOIUrl":"10.1097/RCT.0000000000001683","url":null,"abstract":"<p><strong>Purpose: </strong>The significance of pancreatitis-associated hemorrhage outside the context of a ruptured pseudoaneurysm remains unclear. This study aims to characterize the clinical significance of pancreatic hemorrhage during acute pancreatitis (AP).</p><p><strong>Methods: </strong>This retrospective study included adult patients diagnosed with hemorrhagic pancreatitis (HP) from 2010 to 2021. HP was defined as a clinical diagnosis of AP and the presence of pancreatic or peripancreatic hemorrhage on cross-sectional imaging. Two radiologists assessed the pancreatitis type, degree of necrosis, hemorrhage location, peripancreatic collections, and peripancreatic vessels. Demographic and disease data, AP severity, and treatment decisions from admission to 3 months after discharge were extracted from hospital electronic health records.</p><p><strong>Results: </strong>The study included 36 patients, stratified by AP severity into 12 (33.3%) mild, 13 (36.1%) moderate-severe, and 11 (30.6%) severe cases. Six (16.6%) of the patients experienced clinically significant bleeding, which led to changes in clinical management such as further imaging, modifications to anticoagulation regimens, or both. Among these, 50% (3 of 6) demonstrated active bleeding on further imaging, with 33% (2 of 6) of the bleeding being intrapancreatic. In contrast, 83% (30 of 36) of HP patients did not have clinically significant bleeding, and all but one did not require changes in clinical management. AP-associated splanchnic vein thrombosis occurred in 30.6% (11 of 36) of patients, and anticoagulation in these patients did not result in clinically significant bleeding.</p><p><strong>Conclusions: </strong>HP without clinically significant bleeding does not necessitate changes in clinical management. However, hemorrhage may indicate more severe disease and is associated with a higher incidence of splanchnic vein thrombosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 3","pages":"343-347"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987450","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}