European Journal of Radiology Open最新文献

{"title":"Autoimmune encephalitis: Early and late findings on serial MR imaging and correlation to treatment timepoints","authors":"Mahmoud Abunada ,&nbsp;Nathalie Nierobisch ,&nbsp;Riccardo Ludovichetti ,&nbsp;Cyril Simmen ,&nbsp;Robert Terziev ,&nbsp;Claudio Togni ,&nbsp;Lars Michels ,&nbsp;Zsolt Kulcsar ,&nbsp;Nicolin Hainc","doi":"10.1016/j.ejro.2024.100552","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100552","url":null,"abstract":"<div><h3>Introduction</h3><p>MRI is negative in a large percentage of autoimmune encephalitis cases or lacks findings specific to an antibody. Even rarer is literature correlating the evolution of imaging findings with treatment timepoints. We aim to characterize imaging findings in autoimmune encephalitis at presentation and on follow up correlated with treatment timepoints for this rare disease.</p></div><div><h3>Methods</h3><p>A full-text radiological information system search was performed for “autoimmune encephalitis” between January 2012 and June 2022. Patients with laboratory-identified autoantibodies were included. MRI findings were assessed in correlation to treatment timepoints by two readers in consensus. For statistical analysis, cell-surface vs intracellular antibody groups were assessed for the presence of early limbic, early extralimbic, late limbic, and late extralimbic findings using the χ² test.</p></div><div><h3>Results</h3><p>Thirty-seven patients (female n = 18, median age 58.8 years; range 25.7 to 82.7 years) with 15 different autoantibodies were included in the study. Twenty-three (62%) patients were MRI-negative at time of presentation; 5 of these developed MRI findings on short-term follow up. Of the 19 patients with early MRI findings, 9 (47%) demonstrated improvement upon treatment initiation (7/9 cell-surface group). There was a significant difference (p = 0.046) between the MRI spectrum of cell-surface vs intracellular antibody syndromes as cell-surface antibody syndromes demonstrated more early classic findings of limbic encephalitis and intracellular antibody syndromes demonstrated more late extralimbic abnormalities.</p></div><div><h3>Conclusion</h3><p>MRI can be used to help narrow the differential diagnosis in autoimmune encephalitis and can be used as a monitoring tool for certain subtypes of this rare disease.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100552"},"PeriodicalIF":2.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000078/pdfft?md5=4b3de58428adfb514a1e566566726e3f&pid=1-s2.0-S2352047724000078-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer","authors":"Yaoyao He ,&nbsp;Miao Yang ,&nbsp;Rong Hou ,&nbsp;Shuangquan Ai ,&nbsp;Tingting Nie ,&nbsp;Jun Chen ,&nbsp;Huaifei Hu ,&nbsp;Xiaofang Guo ,&nbsp;Yulin Liu ,&nbsp;Zilong Yuan","doi":"10.1016/j.ejro.2024.100550","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100550","url":null,"abstract":"<div><h3>Objectives</h3><p>To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC).</p></div><div><h3>Methods</h3><p>A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness.</p></div><div><h3>Results</h3><p>In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction.</p></div><div><h3>Conclusion</h3><p>CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100550"},"PeriodicalIF":2.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000054/pdfft?md5=79bcab4e28b787141586eeffc87751ec&pid=1-s2.0-S2352047724000054-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-sectional imaging after pancreatic surgery: The dialogue between the radiologist and the surgeon","authors":"Cesare Maino ,&nbsp;Marco Cereda ,&nbsp;Paolo Niccolò Franco ,&nbsp;Piero Boraschi ,&nbsp;Roberto Cannella ,&nbsp;Luca Vittorio Gianotti ,&nbsp;Giulia Zamboni ,&nbsp;Federica Vernuccio ,&nbsp;Davide Ippolito","doi":"10.1016/j.ejro.2023.100544","DOIUrl":"https://doi.org/10.1016/j.ejro.2023.100544","url":null,"abstract":"<div><p>Pancreatic surgery is nowadays considered one of the most complex surgical approaches and not unscathed from complications. After the surgical procedure, cross-sectional imaging is considered the non-invasive reference standard to detect early and late compilations, and consequently to address patients to the best management possible. Contras-enhanced computed tomography (CECT) should be considered the most important and useful imaging technique to evaluate the surgical site. Thanks to its speed, contrast, and spatial resolution, it can help reach the final diagnosis with high accuracy. On the other hand, magnetic resonance imaging (MRI) should be considered as a second-line imaging approach, especially for the evaluation of biliary findings and late complications. In both cases, the radiologist should be aware of protocols and what to look at, to create a robust dialogue with the surgeon and outline a fitted treatment for each patient.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100544"},"PeriodicalIF":2.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000709/pdfft?md5=2ebb68b066e8322680c77e3bd3684898&pid=1-s2.0-S2352047723000709-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer","authors":"Bo Li ,&nbsp;Jie Su ,&nbsp;Kai Liu,&nbsp;Chunfeng Hu","doi":"10.1016/j.ejro.2024.100549","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100549","url":null,"abstract":"<div><h3>Purpose</h3><p>Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC.</p></div><div><h3>Methods</h3><p>A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.</p></div><div><h3>Results</h3><p>Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890–0.986) compared with the radiomics model (0.829, 95%CI: 0.738–0.898) and the deep learning model (0.935, 95%CI: 0.865–0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779–0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628–0.897) and the deep learning model (0.867, 95% CI: 0.724–0.952).</p></div><div><h3>Conclusion</h3><p>The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100549"},"PeriodicalIF":2.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000042/pdfft?md5=665b39078d34d10cc3d399f816e580ab&pid=1-s2.0-S2352047724000042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases","authors":"Xinna Lv ,&nbsp;Ye Li ,&nbsp;Bing Wang ,&nbsp;Yichuan Wang ,&nbsp;Zexuan Xu ,&nbsp;Dailun Hou","doi":"10.1016/j.ejro.2024.100548","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100548","url":null,"abstract":"<div><h3>Background</h3><p>Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction.</p></div><div><h3>Methods</h3><p>This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models.</p></div><div><h3>Results</h3><p>The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort).</p></div><div><h3>Conclusions</h3><p>Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100548"},"PeriodicalIF":2.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000030/pdfft?md5=a70c992437150ce872f0aba1a3adae00&pid=1-s2.0-S2352047724000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139480051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography","authors":"Si Eun Lee ,&nbsp;Hanpyo Hong ,&nbsp;Eun-Kyung Kim","doi":"10.1016/j.ejro.2023.100545","DOIUrl":"https://doi.org/10.1016/j.ejro.2023.100545","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month.</p></div><div><h3>Methods</h3><p>This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC).</p></div><div><h3>Results</h3><p>Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p &lt; 0.05) compared to stand-alone AI-CAD.</p></div><div><h3>Conclusion</h3><p>Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100545"},"PeriodicalIF":2.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000710/pdfft?md5=c846cac93a8f564a2b410650560d00bd&pid=1-s2.0-S2352047723000710-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139436628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postmortem chest computed tomography in COVID-19: A minimally invasive autopsy method","authors":"Paulo Savoia ,&nbsp;Marcio Valente Yamada Sawamura ,&nbsp;Renata Aparecida de Almeida Monteiro ,&nbsp;Amaro Nunes Duarte-Neto ,&nbsp;Maria da Graça Morais Martin ,&nbsp;Marisa Dolhnikoff ,&nbsp;Thais Mauad ,&nbsp;Paulo Hilário Nascimento Saldiva ,&nbsp;Claudia da Costa Leite ,&nbsp;Luiz Fernando Ferraz da Silva ,&nbsp;Ellison Fernando Cardoso","doi":"10.1016/j.ejro.2024.100546","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100546","url":null,"abstract":"<div><h3>Objectives</h3><p>Performing autopsies in a pandemic scenario is challenging, as the need to understand pathophysiology must be balanced with the contamination risk. A minimally invasive autopsy might be a solution. We present a model that combines radiology and pathology to evaluate postmortem CT lung findings and their correlation with histopathology.</p></div><div><h3>Methods</h3><p>Twenty-nine patients with fatal COVID-19 underwent postmortem chest CT, and multiple lung tissue samples were collected. The chest CT scans were analyzed and quantified according to lung involvement in five categories: normal, ground-glass opacities, crazy-paving, small consolidations, and large or lobar consolidations. The lung tissue samples were examined and quantified in three categories: normal lung, exudative diffuse alveolar damage (DAD), and fibroproliferative DAD. A linear index was used to estimate the global severity of involvement by CT and histopathological analysis.</p></div><div><h3>Results</h3><p>There was a positive correlation between patient mean CT and histopathological severity score indexes - Pearson correlation coefficient (R) = 0.66 (p = 0.0078). When analyzing the mean lung involvement percentage of each finding, positive correlations were found between the normal lung percentage between postmortem CT and histopathology (R=0.65, p = 0.0082), as well as between ground-glass opacities in postmortem CT and normal lungs in histopathology (R=0.65, p = 0.0086), but negative correlations were observed between ground-glass opacities extension and exudative diffuse alveolar damage in histological slides (R=−0.68, p = 0.005). Additionally, it was found is a trend toward a decrease in the percentage of normal lung tissue on the histological slides as the percentage of consolidations in postmortem CT scans increased (R =−0.51, p = 0.055). The analysis of the other correlations between the percentage of each finding did not show any significant correlation or correlation trends (p ≥ 0.10).</p></div><div><h3>Conclusions</h3><p>A minimally invasive autopsy is valid. As the severity of involvement is increased in CT, more advanced disease is seen on histopathology. However, we cannot state that one specific radiological category represents a specific pathological correspondent. Ground-glass opacities, in the postmortem stage, must be interpreted with caution, as expiratory lungs may overestimate disease.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100546"},"PeriodicalIF":2.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000017/pdfft?md5=62b0ba1e43f345bc6fabdea0bce149ea&pid=1-s2.0-S2352047724000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139436627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics","authors":"Yuzhen Xi ,&nbsp;Hao Dong ,&nbsp;Mengze Wang ,&nbsp;Shiyu Chen ,&nbsp;Jing Han ,&nbsp;Miao Liu ,&nbsp;Feng Jiang ,&nbsp;Zhongxiang Ding","doi":"10.1016/j.ejro.2023.100543","DOIUrl":"https://doi.org/10.1016/j.ejro.2023.100543","url":null,"abstract":"<div><h3>Purpose</h3><p>The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC).</p></div><div><h3>Methods</h3><p>In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups.</p></div><div><h3>Results</h3><p>The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66.</p></div><div><h3>Conclusion</h3><p>The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100543"},"PeriodicalIF":2.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000692/pdfft?md5=001f7c2fcfb88e25fc45d76bc97b84b5&pid=1-s2.0-S2352047723000692-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of CINA® LVO artificial intelligence software for detection of large vessel occlusion in brain CT angiography","authors":"Helena Mellander ,&nbsp;Amir Hillal ,&nbsp;Teresa Ullberg ,&nbsp;Johan Wassélius","doi":"10.1016/j.ejro.2023.100542","DOIUrl":"https://doi.org/10.1016/j.ejro.2023.100542","url":null,"abstract":"<div><h3>Objective</h3><p>To systematically evaluate the ability of the CINA® LVO software to detect large vessel occlusions eligible for mechanical thrombectomy on CTA using conventional neuroradiological assessment as gold standard.</p></div><div><h3>Methods</h3><p>Retrospectively, two hundred consecutive patients referred for a brain CTA and two hundred patients that had been subject for endovascular thrombectomy, with an accessible preceding CTA, were assessed for large vessel occlusions (LVO) using the CINA® LVO software. The patients were sub-grouped by occlusion site. The original radiology report was used as ground truth and cases with disagreement were reassessed. Two-by-two tables were created and measures for LVO detection were calculated.</p></div><div><h3>Results</h3><p>A total of four-hundred patients were included; 221 LVOs were present in 215 patients (54 %). The overall specificity was high for LVOs in the anterior circulation (93 %). The overall sensitivity for LVOs in the anterior circulation was 54 % with the highest sensitivity for the M1 segment of the middle cerebral artery (87 %) and T-type internal carotid occlusions (84 %). The sensitivity was low for occlusions in the M2 segment of the middle cerebral artery (13 % and 0 % for proximal and distal M2 occlusions respectively) and in posterior circulation occlusions (0 %, not included in the intended use of the software).</p></div><div><h3>Conclusions</h3><p>LVO detection sensitivity for the CINA® LVO software differs largely depending on the location of the occlusion, with low sensitivity for detection of some LVOs potentially eligible for mechanical thrombectomy. Further development of the software to increase sensitivity to all LVO locations would increase the clinical usefulness.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100542"},"PeriodicalIF":2.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000680/pdfft?md5=c01c43343df5bc80ba1a6999706dc7b0&pid=1-s2.0-S2352047723000680-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative biomarkers for distinguishing bone metastasis and benign bone marrow lesions using turbo spin echo T1- and T2-weighted Dixon imaging at 3.0 T","authors":"Sho Ogiwara,&nbsp;Takeshi Fukuda,&nbsp;Takenori Yonenaga,&nbsp;Akira Ogihara,&nbsp;Hiroya Ojiri","doi":"10.1016/j.ejro.2023.100541","DOIUrl":"https://doi.org/10.1016/j.ejro.2023.100541","url":null,"abstract":"<div><h3>Objective</h3><p>To assess the diagnostic performance and calculate the optimal threshold for quantitative biomarkers to differentiate bone metastasis and benign bone marrow lesions using turbo spin echo (TSE) Dixon images with a 3.0 T scanner.</p></div><div><h3>Materials and methods</h3><p>Each 100 patients diagnosed with bone metastases and variable benign bone marrow lesions on spine MRI were included retrospectively. Images included in-phase (IP), opposed-phase (OP), water images (WI), and fat images (FI) by the TSE Dixon technique with T1WI and T2WI using a 3.0 T scanner. Regions of interest (ROI) of the lesions were manually drawn by two musculoskeletal radiologists independently, and the average signal intensity was recorded. The signal reduction rate from IP to OP (%drop) and a fat fraction (%fat) were calculated.</p></div><div><h3>Results</h3><p>All biomarkers showed a significant difference between metastatic and benign lesions (P &lt; 0.001). When comparing the AUCs, the %drop of T1WI had the highest AUC (0.934). Although the AUC of %fat from T2WI was significantly lower than that of other biomarkers, the %drop of T2WI was not significantly different from the %drop of T1WI (p = 0.339). The optimal threshold of %drop to differentiate metastatic and benign lesions was 22.0 in T1WI and 15.9 in T2WI. The inter-reader agreement was excellent for all biomarkers (0.82–0.86).</p></div><div><h3>Conclusion</h3><p>While %drop of T1WI showed the highest diagnostic performance to differentiate bone metastasis from benign lesions, the %drop of T2WI showed a comparable ability using a threshold 15.9.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"11 ","pages":"Article 100541"},"PeriodicalIF":2.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000679/pdfft?md5=6e2e8903363216b187290fcb6966819c&pid=1-s2.0-S2352047723000679-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138467291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}