Journal of Computer Assisted Tomography最新文献

{"title":"The Clinical Value of the MAR+ Metal Artifact Reduction Algorithm for Postoperative Assessment of Lumbar Internal Fixation.","authors":"Jiayi Fang, Fei Yu, Bin Yang, Guan Wang, Guangyan Si","doi":"10.1097/RCT.0000000000001724","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001724","url":null,"abstract":"<p><strong>Background: </strong>With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic.</p><p><strong>Purpose: </strong>To explore whether the MAR+ method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal.</p><p><strong>Materials and methods: </strong>A total of 57 patients who underwent lumbar spine CT examination after lumbar internal fixation from January to December 2023 in our hospital were retrospectively enrolled. The CT images were reconstructed using MAR+ and non-MAR+ techniques and were subdivided into MAR+ and non-MAR+ groups. The CT number (in Hounsfield units) and the SD noise values of the spinal canal, vertebral body, psoas major muscle, and adjacent fat were measured in the 2 groups of CT images, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective score was evaluated by two diagnostic radiologists using a double-blind method for image quality evaluation of the MAR+ group and the non-MAR+ group, and the image quality was classified on a 5-point scale. The rank-sum test was utilized to compare the subjective and objective scores of the 2 groups.</p><p><strong>Results: </strong>The SD values of the spinal canal (Z=-4.12, P<0.01), vertebral body (Z=-3.81, P<0.01), and psoas major muscle (Z=-3.87, P<0.01) in the MAR+ group were significantly lower than those in the non-MAR+ group (P<0.05). However, the SD values of the adjacent fat (Z=-2.03, P=0.42) in the MAR+ group, although smaller than those in the non-MAR+ group, were not statistically significant. The CNR values of vertebral canal (Z=-2.67, P=0.008) and fat (Z=-2.60, P=0.009) were higher in the MAR+ group than in the non-MAR+ group, whereas the CNR values of the vertebral body (Z=-6.74, P<0.01) in the MAR+ group were smaller than those in the non-MAR+ group, and the difference of all of them was statistically significant (P<0.05). Furthermore, for both CT and SNR values, the MAR group's values were all less than those of the non-MAR group and were statistically significant (P<0.05). The subjective scores of the measurement points were all higher in the MAR+ group than in the non-MAR+ group.</p><p><strong>Conclusions: </strong>The MAR+ technique has a noise reduction effect on different tissues and artifacts are significantly reduced. Although the artifacts caused by metal screws were not completely eliminated, the MAR+ technique was able to reduce the interference of artifacts in the diagnosis of CT images, thus improving their diagnostic quality.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059142","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 Saline Sealing After CT-Guided Lung Biopsy on Pneumothorax and Hemoptysis.","authors":"Xiaoxia Zhang, Jianli An, Jingpeng Wu, Xiuxiu Jing, Hongzhi Lu, Ye Tian","doi":"10.1097/RCT.0000000000001725","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001725","url":null,"abstract":"<p><strong>Objective: </strong>To confirm that saline sealing of the needle trace after computed tomography (CT)-guided lung biopsy reduces the incidence of pneumothorax and chest tube insertion, and to observe its effects on pulmonary hemorrhage and hemoptysis.</p><p><strong>Materials and methods: </strong>Patients who underwent CT-guided lung biopsy at our hospital between January 2018 and January 2024 were included in the study. Patients were divided into 2 groups according to whether the needle trace was sealed with saline after tissue sampling. Patient baseline characteristics, lung lesion factors, procedural factors, pneumothorax rates, chest tube insertion rates, pulmonary hemorrhage rates, and hemoptysis rates were recorded.</p><p><strong>Results: </strong>The incidence of pneumothorax was 28.9% (38/132) and 15.8% (15/95) in groups A (control) and B (with sealed traces), respectively (P=0.002). The incidence of pneumothorax requiring chest tube insertion was significantly lower in group B than in group A (1.1% vs. 6.8%; P=0.048). The incidence of pulmonary hemorrhage was similar between the 2 groups (38.6% vs. 42.1%; P=0.599). No significant difference was observed in the hemoptysis of patients in groups A and B (6.8% vs. 10.5%; P=0.320). In the binary logistic regression analysis, significant risk factors for pneumothorax included lack of saline sealing, smaller lesion size, multiple passes through the pleura, and the lateral decubitus position. Smaller lesions and longer biopsy trace lengths were independent risk factors for hemoptysis.</p><p><strong>Conclusions: </strong>Sealing the needle trace with saline significantly reduced the incidences of pneumothorax and chest tube insertion due to pneumothorax. Moreover, it did not significantly increase the incidence of pulmonary hemorrhage or hemoptysis. This technique is recommended for use in CT-guided lung biopsies.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059098","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":"Combining Low-energy Images in Dual-energy Spectral CT With Deep Learning Image Reconstruction Algorithm to Improve Inferior Vena Cava Image Quality.","authors":"Wei Wei, Yongjun Jia, Ming Li, Nan Yu, Shan Dang, Jian Geng, Dong Han, Yong Yu, Yunsong Zheng, Lihua Fan","doi":"10.1097/RCT.0000000000001713","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001713","url":null,"abstract":"<p><strong>Objective: </strong>To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging.</p><p><strong>Materials and methods: </strong>Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80 keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality. Then, the optimal-energy images were reconstructed again using deep learning image reconstruction medium strength (DLIR-M) and DLIR-H (high strength) algorithms and compared with that of ASiR-V40%.</p><p><strong>Results: </strong>The objective CT value, SD, SNR, and CNR increased with the decrease in energy level, with statistically significant differences (all P<0.05). The 40 keV images had the highest CT values, SNR, and CNR and good diagnostic acceptability, and 40 keV was selected as the best energy level. Compared with ASiR-V40% and DLIR-M, DLIR-H had the lowest SD, highest SNR and CNR, and subjective score (all P<0.001) with good consistencies between the 2 physicians (all k ≥0.75). The 40 keV images with DLIR-H had the highest overall image quality, showing sharper edges of inferior vena cava vessels and clearer lumen in patients with Budd-Chiari syndrome.</p><p><strong>Conclusions: </strong>Compared with the ASiR-V algorithm, DLIR-H significantly reduces image noise and provides the highest CNR and best diagnostic image quality for the 40 keV DEsCT images in imaging inferior vena cava.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059123","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":"Validation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures.","authors":"Michelle Dai, Bryan-Clement Tiu, Jacob Schlossman, Angela Ayobi, Charlotte Castineira, Julie Kiewsky, Christophe Avare, Yasmina Chaibi, Peter Chang, Daniel Chow, Jennifer E Soun","doi":"10.1097/RCT.0000000000001726","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001726","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.</p><p><strong>Methods: </strong>This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports.</p><p><strong>Results: </strong>Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively.</p><p><strong>Conclusions: </strong>CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059146","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":"Utility of Multiparametric Breast MRI Radiomics to Predict Cyclin D1 and TGF-β1 Expression.","authors":"Mengying Zheng, Jiaqi Xu, Shujie Yu, Zhenhua Zhao, Yu Zhang, Mingzhu Wei","doi":"10.1097/RCT.0000000000001717","DOIUrl":"10.1097/RCT.0000000000001717","url":null,"abstract":"<p><strong>Objective: </strong>To develop a machine learning model that integrates clinical features and multisequence MRI radiomics for noninvasively predicting the expression status of prognostic-related factors cyclin D1 and TGF-β1 in breast cancer, providing additional information for the clinical development of personalized treatment plans.</p><p><strong>Methods: </strong>A total of 123 breast cancer patients confirmed by surgical pathology were retrospectively enrolled in our Hospital from January 2016 to July 2022. The patients were randomly divided into a training group (87 cases) and a validation group (36 cases). Preoperative routine and dynamic contrast-enhanced magnetic resonance imaging scans of the breast were performed for treatment subjects. The region of interest was manually outlined, and texture features were extracted using AK software. Subsequently, the LASSO algorithm was employed for dimensionality reduction and feature selection to establish the MRI radiomics labels. The diagnostic efficacy and clinical value were assessed through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).</p><p><strong>Results: </strong>In the cyclin D1 cohort, the area under the receiver operating characteristic (ROC) curve in the clinical prediction model training and validation groups was 0.738 and 0.656, respectively. The multisequence MRI radiomics prediction model achieved an AUC of 0.874 and 0.753 in these respective groups, while the combined prediction model yielded an AUC of 0.892 and 0.785. In the TGF-β1 cohort, the ROC AUC for the clinical prediction model was found to be 0.693 and 0.645 in the training and validation groups, respectively. For the multiseries MRI radiomics prediction model, it achieved an AUC of 0.875 and 0.760 in these respective groups; whereas for the combined prediction model, it reached an AUC of 0.904 and 0.833. Decision curve analysis (DCA) demonstrated that both cohorts indicated a higher clinical application value for the combined prediction model compared with both individual models-clinical prediction model alone or radiomics model.</p><p><strong>Conclusion: </strong>The integration of clinical features and multisequence MRI radiomics in a combined modeling approach holds significant predictive value for the expression status of cyclin D1 and TGF-β1. The model provides a noninvasive, dynamic evaluation method that provides effective guidance for clinical treatment.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965138","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 Evaluation of Noncontrast Magnetic Resonance Enterography for Active Inflammation in Crohn Disease Using Native T 1 and T 2 Mapping.","authors":"Daisuke Morimoto-Ishikawa, Tomoko Hyodo, Yoriaki Komeda, Hiroyuki Fukushima, Makoto Itoh, Yu Ueda, Masatoshi Kudo, Shigeyoshi Saito, Kazunari Ishii","doi":"10.1097/RCT.0000000000001654","DOIUrl":"10.1097/RCT.0000000000001654","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to investigate the utility of native T 1 and T 2 mapping in the bowel to evaluate disease activity in Crohn disease (CD) using endoscopy as the reference standard.</p><p><strong>Methods: </strong>This was a prospective study. Magnetic resonance imaging was performed by using a 1.5-T Philips scanner. We used a modified look-locker inversion recovery and a multiecho gradient-spin-echo sequences for single breath-hold native T 1 and T 2 maps, respectively, for the short-axis image of the intestine, and the measurement at the most severe site was compared with partial Simple Endoscopic Score for Crohn's Disease (pSES-CD, assessed by an expert endoscopist). A pSES-CD ≥ 4 indicated active disease. Statistical analyses were performed using the Student t test, Spearman correlation, and receiver operating characteristic curve analysis.</p><p><strong>Results: </strong>A total of 27 patients (mean age ± standard deviation, 37 ± 18 years; 20 men, 7 women) were included in this study. The native T 1 value of active disease was significantly higher than that of inactive disease (1170.8 ± 100.5 milliseconds vs 924.5 ± 95.3 milliseconds; P = 0.018), but the T 2 value was not significantly different between active and inactive disease (76.1 ± 7.8 milliseconds vs 69.3 ± 10.9 milliseconds; P = 0.424). A good correlation was found between native T 1 value and pSES-CD (ρ = 0.71; P < 0.001) but not between T 2 value and pSES-CD (ρ = 0.06; P = 0.790). The area under the receiver operating characteristic curve for differentiating the disease activity was 0.96 (95% confidence interval [CI]: 0.90-1.00) for T 1 values and 0.68 (95% confidence interval: 0.41-0.96) for T 2 values.</p><p><strong>Conclusions: </strong>Native T 1 mapping could be potentially used as a noninvasive method to differentiate disease activity in patients with CD and may be superior to T 2 mapping for this purpose.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"1-8"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982414","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 Femoral Head Avascular Necrosis With Virtual Noncalcium Dual-Energy Computed Tomography.","authors":"Muhsin Ozgun Ozturk, Mecit Kantarcı, Sonay Aydin, Volkan Kızılgöz, Nizamettin Kockara, Volkan Gur","doi":"10.1097/RCT.0000000000001655","DOIUrl":"10.1097/RCT.0000000000001655","url":null,"abstract":"<p><strong>Objective: </strong>Our aim was to investigate the effectiveness of the dual-energy computed tomography (DECT) virtual noncalcium (VNCa) technique in avascular necrosis (AVN) for detecting bone marrow edema (BME) and staging.</p><p><strong>Methods: </strong>This prospective study included adult patients diagnosed with unilateral or bilateral femoral head AVN between January 2023 and December 2023, who had magnetic resonance imaging (MRI) and DECT. Two participants were excluded from the study due to undergoing surgical procedures during the period between the scans. Two reviewers, blinded to MRI images and clinical data, visually examined color-coded VNCa pictures to assess BME using a binary classification (0 = normal bone marrow, 1 = BME). Same 2 reviewers also used color-coded and nonmapped images to stage AVN in accordance to the \"Association for Research on Osseous Circulation\" (ARCO) staging system. Interobserver agreements for the visual evaluation and staging were calculated with κ coefficient. Following a visual assessment of BME and the staging of AVN, same 2 reviewers conducted CT density measurements on regions of BME regions utilizing DECT noncalcium images. An independent third investigator (reference standard) utilized MRI, x-ray, and clinical data to confirm the definitive diagnosis and staging of AVN. A P value less than 0.05 was considered statistically significant.</p><p><strong>Results: </strong>Fifty patients (28 men, 22 women, mean age: 44.2 ± 13.1 years, range: 25-75 years) were included in the final analysis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the VNCa technique in detecting BME were 96.0%, 94.4%, 97.9%, 89.4%, and 95.6%, respectively, for reviewer 1; and 96.0%, 88.9%, 96.0%, 88.9%, and 94.1%, respectively, for reviewer 2. Interobserver agreement was almost perfect ( κ = 0.84). Both reviewer 1 and reviewer 2 accurately classified 92.7% of the AVNs. The density measurements showed a statistically significant difference ( P = 0.001) between the edema regions and the normal marrow regions. No statistically significant difference was observed in the density measurements of edema regions at different stages ( P = 0.13).</p><p><strong>Conclusions: </strong>DECT VNCa technique exhibits excellent performance in detecting BME in hip AVN cases, as well as accurately determining the stage of AVN.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"133-139"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988086","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":"Uterine Sarcoma or Degenerating Fibroid? Validating the New Consensus Magnetic Resonance Imaging Algorithm for Evaluating Atypical Uterine Masses.","authors":"Jeanne M Horowitz, Camila Lopes Vendrami, Yuri S Velichko, Aja I Green-Walker, Linda C Kelahan, Anugayathri Jawahar, Emma L Barber, Elisheva D Shanes, Frank H Miller, Hannah S Recht","doi":"10.1097/RCT.0000000000001656","DOIUrl":"10.1097/RCT.0000000000001656","url":null,"abstract":"<p><strong>Objective: </strong>The aim of the study is to assess the validity of a recently published consensus magnetic resonance imaging (MRI) diagnostic algorithm for differentiating degenerating leiomyomas from uterine sarcomas and other atypical appearing uterine malignancies.</p><p><strong>Methods: </strong>Atypical uterine masses on pelvic MRI were identified using a radiology report search engine and teaching files with the keywords \"atypical leiomyoma,\" \"atypical fibroid,\" and \"sarcoma.\" All cases were pathology-proven. Two radiologists blinded to clinical, surgical, and pathologic reports retrospectively and independently reviewed 40 pelvic MRI examinations dated 1/2007-9/2022 to determine whether the masses appeared benign or malignant, using the 2022 consensus atypical uterine mass flow chart. Imaging features assessed included intermediate/high signal intensity (SI) at T2-weighted imaging, high diffusion weighted imaging SI (equal or higher SI than endometrium or lymph nodes on high b value imaging), apparent diffusion coefficient (ADC) value ≤0.905 × 10 -3 mm 2 /s, peritoneal metastases, and abnormal lymph nodes.</p><p><strong>Results: </strong>Among the 40 atypical uterine mass cases reviewed, 24 masses were benign (22 leiomyomas, 1 adenomyoma, and 1 borderline ovarian tumor) and 16 masses were malignant (6 leiomyosarcomas, 6 carcinosarcomas, 2 endometrial stromal sarcomas, 1 high-grade adenosarcoma, and 1 low-grade uterine sarcoma). Sensitivity, specificity, positive predictive value, and negative predictive value of whether a mass was benign or malignant were 75%, 95.8%, 92.3%, and 85% for reader 1, and 81.2%, 91.7%, 86.7%, and 88% for reader 2, respectively. Interrater agreement was strong, with a kappa statistic of 0.89. When excluding nonleiomyosarcoma uterine malignancies, sensitivity and negative predictive value improved to 100%.</p><p><strong>Conclusions: </strong>The new consensus pelvic MRI algorithm for evaluating atypical uterine masses has good specificity, sensitivity, positive predictive value, and negative predictive value for determining malignancy, particularly for uterine sarcomas that are leiomyosarcomas. However, if ADC value is near but not below 0.905 × 10 -3 mm 2 /s, the mass may still be malignant, especially if a b value lower than 1000 is used. If the atypical uterine mass is predominantly endometrial, morphological features on T2 and postgadolinium sequences should guide suspicion, as some atypical appearing nonleiomyosarcoma uterine malignancies may have an ADC value greater than 0.905 × 10 -3 mm 2 /s.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"57-63"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390922","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 Novel Approach to Detecting Contrast Extravasation in Computed Tomography: Evaluating the Injection Pressure-to-Injection Rate Ratio.","authors":"Naoki Kobayashi, Takeshi Nakaura, Kaori Shiraishi, Hiroyuki Uetani, Yasunori Nagayama, Masafumi Kidoh, Seitaro Oda, Daisuke Sakabe, Ryuji Ikeda, Masahiro Hatemura, Michiyo Murakami, Yoshinori Funama, Toshinori Hirai","doi":"10.1097/RCT.0000000000001614","DOIUrl":"10.1097/RCT.0000000000001614","url":null,"abstract":"<p><strong>Objective: </strong>The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography.</p><p><strong>Methods: </strong>We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves.</p><p><strong>Results: </strong>Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 ( P = 0.270), 0.712 versus 0.678 at t = 2 ( P = 0.305), 0.758 versus 0.693 at t = 3 ( P = 0.032), 0.776 versus 0.688 at t = 4 ( P = 0.005), 0.810 versus 0.699 at t = 5 ( P = 0.002), and 0.811 versus 0.706 at t = 6 ( P = 0.002).</p><p><strong>Conclusions: </strong>The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"125-132"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140891816","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":"Usefulness of Picture Archiving and Communication System-Based Quantitative Ultrasound Measurements in Evaluation of Allograft Dysfunction in Patients With Liver Transplantation.","authors":"Iclal Erdem Toslak, Cara Joyce, Joseph H Yacoub","doi":"10.1097/RCT.0000000000001647","DOIUrl":"10.1097/RCT.0000000000001647","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to assess the usefulness of picture archiving and communication system (PACS)-based quantitative grayscale ultrasonography (US) measurements in detecting allograft dysfunction in posttransplant patients.</p><p><strong>Methods: </strong>In this retrospective study, 116 patients with liver transplantation who underwent biopsy for allograft evaluation were recruited from the database. All participants had US images prior to procedure. Normal, acute cellular rejection (ACR), recurrent hepatitis (Hep), or combined (ACR/Hep) groups were generated based on pathology results. Region of interests were drawn for liver and rectus abdominus muscle to perform quantitative US analysis. The liver/muscle mean ratio (L/M) and heterogeneity index (HI; liver standard deviation/liver mean) were obtained. The ratios of groups were compared, and receiver-operating-characteristic analysis was performed.</p><p><strong>Results: </strong>There was a significant difference between normal (n = 16) and each of other groups (ACR, 39; Hep, 36; combined, 25) for L/M and HI ( P < 0.05). No significant difference was detected between ACR, Hep, and combined groups. The areas under the curve for L/M and HI were 0.755 (moderate) and 0.817 (good), respectively. To differentiate abnormal (ACR, Hep, and combined) from normal allografts sensitivity, specificity, PPV, and NPV were 50.0%, 87.5%, 96.2%, and 21.9% for cut point of L/M ≥1 and 84.0%, 68.8%, 94.4%, and 40.7% for cut point of HI ≥0.2 with odds ratios of 7.52 (for L/M ≥1) and 13.10 (for HI ≥0.2), respectively ( P < 0.01).</p><p><strong>Conclusions: </strong>L/M has moderate and HI has good discrimination of normal from abnormal allograft in liver transplant patients. PACS-based quantitative US measurements is an objective, easy to use, noninvasive auxiliary tool to discriminate hepatic allograft dysfunction.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"34-41"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878786","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}