Academic Radiology最新文献

{"title":"A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.","authors":"Yu Mao, Xin Kong, Yuqi Luo, Fengjun Xi, Yan Li, Jun Ma","doi":"10.1016/j.acra.2024.11.044","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.044","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sellar region.</p><p><strong>Methods: </strong>This study included 348 patients with histologically confirmed PA (n = 139) and ACP (n = 209). Data were randomly divided into training and testing cohorts in a 7:3 ratio. Pre-trained ResNet50 network was utilized to extract DTL features from T1WI, T2WI, and CET1, while radiomics features (Rad) were extracted from manually delineated images of the same modalities. The fusion feature set (DLR) was constructed by integrating these features. Semantic features were used to develop clinical models. Pearson rank correlation and The least absolute shrinkage and selection operator regression were used for feature selection, and K-nearest neighbor algorithm was applied to establish the model. The performance of the model was evaluated using receiver operating characteristic curve. DeLong's test was performed to assess differences between models, and decision curve analysis was conducted to evaluate the clinical utility of the models.</p><p><strong>Results: </strong>The DLR model achieved AUC values of 0.945 (95% CI, 0.9149-0.9760) in the training cohort and 0.929 (95% CI, 0.8824-0.9762) in the testing cohort, significantly higher than those of models using DTL features, Rad features, or clinical features alone.</p><p><strong>Conclusion: </strong>The fusion model based on MRI deep transfer learning and radiomics (DLR) demonstrated high accuracy and clinical utility in discriminating between PA and ACP, providing an effective tool for the non-invasive diagnosis of these two diseases.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics of multimodal ultrasound for early prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer.","authors":"Cai-Feng Wan, Zhuo-Yun Jiang, Yu-Qun Wang, Lin Wang, Hua Fang, Ye Jin, Qi Dong, Xue-Qing Zhang, Li-Xin Jiang","doi":"10.1016/j.acra.2024.11.012","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.012","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To construct and validate a clinical-radiomics model based on radiomics features extracted from two-stage multimodal ultrasound and clinicopathologic information for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients treated with NAC.</p><p><strong>Materials and methods: </strong>Consecutive women with biopsy-proven breast cancer undergoing multimodal US pretreatment and after two cycles of NAC and followed by surgery between January 2014 and November 2023 were retrospectively collected for clinical-radiomics model construction (n = 274) and retrospective test (n = 134). The predictive performance of it was further tested in a subsequent prospective internal test set recruited between January 2024 to July 2024 (n = 76). Finally, a total of 484 patients were enrolled. The clinical-radiomics model predictive performance was compared with radiomics model, clinical model and radiologists' visual assessment by area under the receiver operating characteristic curve (AUC) analysis and DeLong test.</p><p><strong>Results: </strong>The proposed clinical-radiomics model obtained the AUC values of 0.92 (95%CI: 0.88, 0.94) and 0.85 (95%CI: 0.79, 0.89) in retrospective and prospective test sets, respectively, which were significantly higher than that those of the radiomics model (AUCs: 0.75-0.85), clinical model (AUCs: 0.68-0.72) and radiologists' visual assessments (AUCs:0.59-0.68) (all p < 0.05). In addition, the predictive efficacy of the radiologists was improved under the assistance of the clinical-radiomics model significantly.</p><p><strong>Conclusion: </strong>The clinical-radiomics model developed in this study, which integrated clinicopathologic information and two-stage multimodal ultrasound features, was able to early predict pCR to NAC in breast cancer patients with favorable predictive effectiveness.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.","authors":"Yaning Wang, Jingfeng Zhang, Mingyang Li, Zheng Miao, Jing Wang, Kan He, Qi Yang, Lei Zhang, Lin Mu, Huimao Zhang","doi":"10.1016/j.acra.2024.11.056","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.056","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning on non-contrast computed tomography (CT) and unstructured text data, enhancing the speed and accuracy of solid organ assessments.</p><p><strong>Materials and methods: </strong>Data were collected from patients undergoing abdominal CT scans. The SMART model (Screening for Multi-organ Assessment in Rapid Trauma) classifies trauma using text data (SMART_GPT), non-contrast CT scans (SMART_Image), or both. SMART_GPT uses the GPT-4 embedding API for text feature extraction, whereas SMART_Image incorporates nnU-Net and DenseNet121 for segmentation and classification. A composite model was developed by integrating multimodal data via logistic regression of SMART_GPT, SMART_Image, and patient demographics (age and gender).</p><p><strong>Results: </strong>This study included 2638 patients (459 positive, 2179 negative abdominal trauma cases). A trauma-based dataset included 1006 patients with 1632 real continuous data points for testing. SMART_GPT achieved a sensitivity of 81.3% and an area under the receiver operating characteristic curve (AUC) of 0.88 based on unstructured text data. SMART_Image exhibited a sensitivity of 87.5% and an AUC of 0.81 on non-contrast CT data, with the average sensitivity exceeding 90% at the organ level. The integrated SMART model achieved a sensitivity of 93.8% and an AUC of 0.88. In emergency department simulations, SMART reduced waiting times by over 64.24%.</p><p><strong>Conclusion: </strong>SMART provides rapid, objective trauma diagnostics, improving emergency care efficiency, reducing patient wait times, and enabling multimodal screening in diverse emergency contexts.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-Defined Pectoralis Muscle Density Predicts 30-Day Mortality in Hospitalized Patients with COVID-19: A Nationwide Multicenter Study.","authors":"Andreas Michael Bucher, Julius Behrend, Constantin Ehrengut, Lukas Müller, Tilman Emrich, Dominik Schramm, Alena Akinina, Roman Kloeckner, Malte Sieren, Lennart Berkel, Christiane Kuhl, Marwin-Jonathan Sähn, Matthias A Fink, Dorottya Móré, Bohdan Melekh, Hakan Kardas, Felix G Meinel, Hanna Schön, Norman Kornemann, Diane Miriam Renz, Nora Lubina, Claudia Wollny, Marcus Both, Joe Watkinson, Sophia Stöcklein, Andreas Mittermeier, Gizem Abaci, Matthias May, Lisa Siegler, Tobias Penzkofer, Maximilian Lindholz, Miriam Balzer, Moon-Sung Kim, Christian Römer, Niklas Wrede, Sophie Götz, Julia Breckow, Jan Borggrefe, Hans Jonas Meyer, Alexey Surov","doi":"10.1016/j.acra.2024.11.054","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.054","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting.</p><p><strong>Materials and methods: </strong>This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann-Whitney-U test and Fisher's exact test. Univariable and multivariable logistic regression analyses were performed.</p><p><strong>Results: </strong>The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR=2.97, 95%-CI: 1.52-5.80, p=0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR=2.96, 95%-CI: 1.42-6.18, p=0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality. For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR=2.72, 95%-CI: 1.17;6.30, p=0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality.</p><p><strong>Conclusion: </strong>In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study.","authors":"Huiyuan Zhu, Zike Huang, Qunhui Chen, Weiling Ma, Jiahui Yu, Shiqing Wang, Guangyu Tao, Jun Xing, Haixin Jiang, Xiwen Sun, Jing Liu, Hong Yu, Lin Zhu","doi":"10.1016/j.acra.2024.11.042","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.042","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.</p><p><strong>Materials and methods: </strong>Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard. Objective assessment, including image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), and subjective assessment using five-point scales by five radiologists were performed. Detection and false-positive rate of subsolid nodules, and morphologic features of nodules were recorded.</p><p><strong>Results: </strong>102 patients (mean age, 57.0 ± 12.3 years) with 358 subsolid nodules in SDCT were enrolled. The mean effective dose of SDCT and LDCT were 5.37 ± 0.80mSv and 0.86 ± 0.14mSv, respectively (P < 0.001). DLIR-H showed the lowest noise, highest CNRs, SNRs, and subjective scores among LDCT groups (all P < 0.001), almost approaching comparability with SDCT. The detection rates for DLIR-H, DLIR-M, ASIR-V-50%, and FBP were 76.5%, 76.3%, 83.8%, and 72.1%, respectively (P < 0.001), with false-positive rate of 2.5%, 2.2%, 8.3%, and 1.1%, respectively (P < 0.001). DLIR-H showed the highest detection rates for morphologic features (79.4%-95.2%) compared to DLIR-M (74.6%-88.9%), ASIR-V-50% (72.0%-88.4%), and FBP (66.1%-84.1%) (all P ≤ 0.001).</p><p><strong>Conclusion: </strong>Sub-milliSievert LDCT with DLIR-H offers substantial dose reduction without compromising image quality. It is promising for evaluating subsolid nodules with a high detection rate and better identification of morphologic features.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Angioleiomyomas of the Extremities and Trunk: An Observational Study.","authors":"Rebecca H Chun, Akriti Khanna, Katrina N Glazebrook, Judith Jebastin Thangaiah, Christin A Tiegs-Heiden","doi":"10.1016/j.acra.2024.11.061","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.061","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Angioleiomyomas are benign perivascular tumors that originate from the tunica media of blood vessels. While frequently described in the head, neck, and uterus, angioleiomyomas can manifest in various regions throughout the body. The purpose of this study was to review the history and imaging features of angioleiomyomas of the trunk and extremities.</p><p><strong>Materials and methods: </strong>Patients with pathologically proven angioleiomyomas at our institution were retrospectively identified. Clinical information was obtained by chart review. Any available imaging of the tumor was reviewed.</p><p><strong>Results: </strong>This study includes 191 patients with angioleiomyoma of the trunk or extremities, 87 with imaging of the tumor. Mean age at presentation was 55.5 years and 59.7% of patients were female. The tumor was painful in 88.9% of patients. Most lesions were in the lower extremity (79.1%), followed by the upper extremity (17.8%) and trunk (3.1%). A nonspecific soft tissue mass was visible radiographically in 27.4% of cases, with calcifications in 1.8%. On ultrasound, the tumor was always hypoechoic, with internal vascularity in 93.8%. Most tumors were T1 isointense and T2 hyperintense relative to skeletal muscle (92.9%) and enhanced (95.8%). CT showed a soft tissue density mass in all cases. On cross-sectional imaging, the mass was directly adjacent to a blood vessel in 83.1% of cases.</p><p><strong>Discussion: </strong>Key imaging features of angioleiomyomas include a soft tissue mass with adjacent blood vessel on cross-sectional imaging. Ultrasound shows a hypoechoic mass with internal vascularity. They are typically T1 isointense, T2 hyperintense enhancing masses which may have a dark reticular sign and/or hypointense peripheral rim. Recognizing these features may help include angioleiomyoma in the differential diagnosis.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mock Residency Interviews: The Role of Medical Students and Residents.","authors":"Reid D Masterson, Shaun D Grega, Katrina M Fliotsos, Atul Agarwal, Richard B Gunderman","doi":"10.1016/j.acra.2024.11.058","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.058","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting High-risk Lung Adenocarcinoma in Solid and Part-solid Nodules on Low-dose CT: A Multicenter Study.","authors":"Jieke Liu, Yong Li, Yu Long, Yongji Zheng, Junqiang Liang, Wei Lin, Ling Guo, Haomiao Qing, Peng Zhou","doi":"10.1016/j.acra.2024.11.059","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.059","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>High-grade patterns, visceral pleural invasion, lymphovascular invasion, spread through air spaces, and lymph node metastasis are high-risk factors and associated with poor prognosis in lung adenocarcinomas (LUADs). This study aimed to construct and validate a radiomic model and a radiographic model derived from low-dose CT (LDCT) for predicting high-risk LUADs in solid and part-solid nodules.</p><p><strong>Materials and methods: </strong>This study retrospectively enrolled 658 pathologically confirmed LUADs from July 2018 to December 2022 from four centers, which were divided into training set (n=411), internal validation set (n=139), and external validation set (n=108). Radiomic features and radiographic features including maximal diameter, consolidation/tumor ratio (CTR), and semantic features, were obtained to construct a radiomic model and a radiographic model through multivariable logistic regression. Area under receiver operating characteristic curve (AUC) was utilized to assess the diagnostic performance of the models.</p><p><strong>Results: </strong>Three radiomic features (GLCM_Correlation, GLSZM_SmallAreaEmphasis, and GLDM_LargeDependenceHighGrayLevelEmphasis) and four radiographic features (maximal diameter, CTR, spiculation, and pleural indentation) were selected to build models. The radiomic model yielded AUCs of 0.916 in the internal validation set and 0.938 in the external validation set, which were significantly higher than the AUCs of the radiographic model (0.916 vs. 0.868, P=0.014 and 0.938 vs. 0.880, P=0.002).</p><p><strong>Conclusion: </strong>Our LDCT-based radiomic model enabled non-invasive identification of high-risk LUADs in solid and part-solid nodules with good diagnostic performance and might assist in case-specific decision-making in lung cancer screening.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From ¹⁸F-FDG PET/CT Based on Interpretable Machine Learning.","authors":"Furui Duan, Minghui Zhang, Chunyan Yang, Xuewei Wang, Dalong Wang","doi":"10.1016/j.acra.2024.11.037","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.037","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). The model's interpretability was enhanced using Shapley additive explanations (SHAP).</p><p><strong>Methods: </strong>A total of 248 NSCLC patients who underwent preoperative PET/CT scans were included and divided into training, test, and external validation sets. Radiomics features were extracted from segmented tumor regions on PET/CT images, and deep learning features were generated using the ResNet50 architecture. Feature selection was performed using minimum-redundancy maximum-relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) algorithm. Four models-clinical, radiomics, deep learning radiomics (DL_radiomics), and combined model-were constructed using the XGBoost algorithm and evaluated based on diagnostic performance metrics, including area under the receiver operating characteristic curve (AUC), accuracy, F1 score, sensitivity, and specificity. Shapley Additive exPlanations (SHAP) was used for model interpretability.</p><p><strong>Results: </strong>The combined model achieved the highest AUC in the test set (AUC=0.853), outperforming the clinical (AUC=0.758), radiomics (AUC=0.831), and DL_radiomics (AUC=0.834) models. Decision curve analysis (DCA) demonstrated that the combined model offered greater clinical net benefits. SHAP was used for global interpretation, and the summary plot indicated that the features ct_original_glrlm_LongRunHighGrayLevelEmphasis, and pet_gradient_glcm_lmc1 were the most important for the model's predictions.</p><p><strong>Conclusion: </strong>The combined model, combining clinical, radiomics, and deep learning features from PET/CT, significantly improved the accuracy of LNM prediction in NSCLC patients. SHAP-based interpretability provided valuable insights into the model's decision-making process, enhancing its potential clinical application for preoperative decision-making in NSCLC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-individual Differences in Pericoronary Fat Attenuation Index Measurements Between Photon-counting and Energy-integrating Detector Computed Tomography.","authors":"Giuseppe Tremamunno, Milan Vecsey-Nagy, Muhammad Taha Hagar, U Joseph Schoepf, Jim O'Doherty, Julian A Luetkens, Daniel Kuetting, Alexander Isaak, Akos Varga-Szemes, Tilman Emrich, Dmitrij Kravchenko","doi":"10.1016/j.acra.2024.11.055","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.055","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The purpose of this study was to explore intra-individual differences in pericoronary adipose tissue (PCAT) fat attenuation index (FAI) between photon-counting detector (PCD)- and energy-integrating detector (EID)-CT.</p><p><strong>Material and methods: </strong>Patients were prospectively enrolled for a PCD-CT research scan within 30 days of EID-CT. Both acquisitions were reconstructed using a Qr36 kernel at 0.6 mm slice thickness (EID and PCD-down-sampled [DS]) and at 0.2 mm ultra-high resolution (UHR) for the PCD-CT. Iterative reconstruction was turned \"off\" (filter back projection used as alternative reconstruction method) or set to a recommended level in current literature. Coronary PCAT FAI was measured automatically using established thresholds of -190 to -30 HU at a set distance and radius. Statistical testing was performed using repeated-measures ANOVA and Bonferroni's multiple comparison tests (p significance was determined to be <0.003).</p><p><strong>Results: </strong>In total, 40 patients (mean age 68±8 years, 32 males [80%]) were included for analysis. Absolute FAI measurements differed significantly for all vessels between all reconstructions in the ANOVA comparison (all p<.001). The FAI decreased going from EID-CT to PCD-DS, to PCD-UHR with iterative reconstruction turned off (e.g. right coronary artery: EID-CT: -76.5±8.9 vs -80.9±7.0 vs -88.3±6.7 HU, respectively; p < 0.001). The mean FAI of datasets using iterative reconstruction did not demonstrate significant differences on multiple comparisons (e.g. left circumflex artery: EID: -65.7±8.5; PCD-DS: -66.0±7.4; PCD-UHR: -67.8±7.0 HU, respectively; p>0.06).</p><p><strong>Conclusion: </strong>Intra-individual absolute PCAT FAI measurements differ significantly between EID- and PCD-CT when controlling for reconstruction kernel and slice thickness. However, the use of iterative reconstruction minimizes most differences in FAI, enabling inter-scanner comparability.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}