European Journal of Radiology Open最新文献

{"title":"The predictive power of baseline metabolic and volumetric [18F]FDG PET parameters with different thresholds for early therapy failure and mortality risk in DLBCL patients undergoing CAR-T-cell therapy","authors":"Emil Novruzov ,&nbsp;Helena A. Peters ,&nbsp;Kai Jannusch ,&nbsp;Guido Kobbe ,&nbsp;Sascha Dietrich ,&nbsp;Johannes C. Fischer ,&nbsp;Jutta Rox ,&nbsp;Gerald Antoch ,&nbsp;Frederik L. Giesel ,&nbsp;Christina Antke ,&nbsp;Ben-Niklas Baermann ,&nbsp;Eduards Mamlins","doi":"10.1016/j.ejro.2024.100619","DOIUrl":"10.1016/j.ejro.2024.100619","url":null,"abstract":"<div><h3>Objective</h3><div>[¹⁸F]FDG imaging is an integral part of patient management in CAR-T-cell therapy for recurrent or therapy-refractory DLBCL. The calculation methods of predictive power of specific imaging parameters still remains elusive. With this retrospective study, we sought to evaluate the predictive power of the baseline metabolic parameters and tumor burden calculated with automated segmentation via different thresholding methods for early therapy failure and mortality risk in DLBCL patients.</div></div><div><h3>Materials and methods</h3><div>Eighteen adult patients were enrolled, who underwent CAR-T-cell therapy accompanied by at least one pretherapeutic and two posttherapeutic [¹⁸F]FDG PET scans within 30 and 90 days between December 2018 and October 2023. We performed single-click automatic segmentation within VOIs in addition to extracting the SUV parameters to calculate the MTVs and TLGs by applying thresholds based on the concepts of a fixed absolute threshold with an SUV_max &gt; 4.0, a relative absolute threshold with an isocontour of &gt; 40 % of the SUV_max, a background threshold involving the addition of the liver SUV value and its 2 SD values, and only the liver SUV value.</div></div><div><h3>Results</h3><div>For early therapy failure, baseline metabolic parameters such as the SUV_max, SUV_peak and SUV_mean tended to have greater predictive power than did the baseline metabolic burden. However, the baseline metabolic burden was superior in the prediction of mortality risk regardless of the thresholding method used.</div></div><div><h3>Conclusion</h3><div>This study revealed that automated delineation methods of metabolic tumor burden using different thresholds do not differ in outcome substantially. Therefore, the current clinical standard with a fixed absolute threshold value of SUV &gt; 4.0 seems to be a feasible option.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100619"},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11719856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972521","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":"Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images","authors":"Yingqi Luo ,&nbsp;Qingqi Yang ,&nbsp;Jinglang Hu ,&nbsp;Xiaowen Qin ,&nbsp;Shengnan Jiang ,&nbsp;Ying Liu","doi":"10.1016/j.ejro.2024.100624","DOIUrl":"10.1016/j.ejro.2024.100624","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).</div></div><div><h3>Methods</h3><div>This study included 185 patients who underwent ¹⁸F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the \"reference standard\". Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses.</div></div><div><h3>Results</h3><div>This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19–9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician.</div></div><div><h3>Conclusion</h3><div>This deep learning model demonstrated high sensitivity in detecting FLLs and effectively differentiated between benign and malignant lesions.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100624"},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972520","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 enabled near-isotropic CAIPIRINHA VIBE in the nephrogenic phase improves image quality and renal lesion conspicuity","authors":"Qinxuan Tan ,&nbsp;Jingyu Miao ,&nbsp;Leila Nitschke ,&nbsp;Marcel Dominik Nickel ,&nbsp;Markus Herbert Lerchbaumer ,&nbsp;Tobias Penzkofer ,&nbsp;Sebastian Hofbauer ,&nbsp;Robert Peters ,&nbsp;Bernd Hamm ,&nbsp;Dominik Geisel ,&nbsp;Moritz Wagner ,&nbsp;Thula Cannon Walter-Rittel","doi":"10.1016/j.ejro.2024.100622","DOIUrl":"10.1016/j.ejro.2024.100622","url":null,"abstract":"<div><h3>Background</h3><div>Deep learning (DL) accelerated controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), provides high spatial resolution T1-weighted imaging of the upper abdomen. We aimed to investigate whether DL-CAIPIRINHA-VIBE can improve image quality, vessel conspicuity, and lesion detectability compared to a standard CAIPIRINHA-VIBE in renal imaging at 3 Tesla.</div></div><div><h3>Methods</h3><div>In this prospective study, 50 patients with 23 solid and 45 cystic renal lesions underwent MRI with clinical MR sequences, including standard CAIPIRINHA-VIBE and DL-CAIPIRINHA-VIBE sequences in the nephrographic phase at 3 Tesla. Two experienced radiologists independently evaluated both sequences and multiplanar reconstructions (MPR) of the sagittal and coronal planes for image quality with a Likert scale ranging from 1 to 5 (5 =best). Quantitative measurements including the size of the largest lesion and renal lesion contrast ratios were evaluated.</div></div><div><h3>Results</h3><div>DL-CAIPIRINHA-VIBE compared to standard CAIPIRINHA-VIBE showed significantly improved overall image quality, higher scores for renal border delineation, renal sinuses, vessels, adrenal glands, reduced motion artifacts and reduced perceived noise in nephrographic phase images (all p &lt; 0.001). DL-CAIPIRINHA-VIBE with MPR showed superior lesion conspicuity and diagnostic confidence compared to standard CAIPIRINHA-VIBE. However, DL-CAIPIRINHA-VIBE presented a more synthetic appearance and more aliasing artifacts (p &lt; 0.023). The mean size and signal intensity of renal lesions for DL-CAIPIRINHA-VIBE showed no significant differences compared to standard CAIPIRINHA-VIBE (p &gt; 0.9).</div></div><div><h3>Conclusions</h3><div>DL-CAIPIRINHA-VIBE is well suited for kidney imaging in the nephrographic phase, provides good image quality, improved delineation of anatomic structures and renal lesions.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100622"},"PeriodicalIF":1.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932876","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":"An online national quality assessment survey of prostate MRI reading: interreader variability in prostate volume measurement and PI-RADS classification","authors":"Jonas Wallström ,&nbsp;Erik Thimansson ,&nbsp;Jim Andersson ,&nbsp;Mathias Karlsson ,&nbsp;Sophia Zackrisson ,&nbsp;Ola Bratt ,&nbsp;Fredrik Jäderling","doi":"10.1016/j.ejro.2024.100625","DOIUrl":"10.1016/j.ejro.2024.100625","url":null,"abstract":"<div><h3>Background</h3><div>High-quality assessment of prostate MRI is fundamental in both clinical practice and screening. There is a lack of national level data on variability in prostate volume measurement and PI-RADS assessment. Methods of quality assurance need to be developed.</div></div><div><h3>Methods</h3><div>All Swedish radiology departments were invited to participate in an external quality assurance of prostate MRI reading. Ten prostate MRI cases were selected by an expert panel to reflect common findings. Readers measured whole gland volume (ellipsoid formula method) and assigned a PI-RADS score in a web-based PACS with full clinical functionality. Expert consensus was used as reference standard. Descriptive statistics were used to show the distribution of volume measurements and PSA density. Reader agreement was assessed using percentages and kappa scores. A feedback document was sent to all participants upon completion of the quality assurance program.</div></div><div><h3>Results</h3><div>Forty-three radiologists representing 17 departments read at least 7 out of 10 cases. The median difference in prostate volume assessment compared to the reference volume for the 10 cases ranged from −23 mL to + 6 mL. Per case agreement ranged from 33 % to 86 % for the assigned PI-RADS score and from 35 % to 98 % for PI-RADS 1–3 versus PI-RADS 4–5. Interreader agreement was moderate with a median kappa score of 0.53 (IQR 0.48–0.62).</div></div><div><h3>Conclusion</h3><div>This online model for national quality assurance programs was feasible. Rather large per-case reader variations in prostate volume assessment and PI-RADS scoring were shown. To reduce variability in clinical practice, systematic interreader comparisons should be encouraged.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100625"},"PeriodicalIF":1.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932875","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":"Role of pre-procedure CCTA in predicting failed percutaneous coronary intervention for chronic total occlusions","authors":"Hua Zhou ,&nbsp;Xiaojun Fan ,&nbsp;Mingyuan Yuan ,&nbsp;Wei Wang,&nbsp;Qiyuan Wu","doi":"10.1016/j.ejro.2024.100616","DOIUrl":"10.1016/j.ejro.2024.100616","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to identify major lesion characteristics of chronic total occlusions (CTOs) that predict failed percutaneous coronary intervention (PCI) using pre-procedure coronary computed tomography angiography (CCTA) in combination with conventional coronary angiography (CCA).</div></div><div><h3>Methods</h3><div>Consecutive patients with at least one CTO of the native coronary arteries received CCTA and CCA-guided PCI, with computed tomography performed before or during PCI.</div></div><div><h3>Results</h3><div>A total of 76 patients with CTO were included in this study. 76 patients were divided into successful and failed PCI groups based on their PCI outcome. There were 62 (81.58 %) patients in the successful PCI group and 14 (18.42 %) in the failed PCI group. The occlusion length ≥20 mm, ostial or bifurcation lesions, negative remodeling, microchannels, and good collateral vessels were the CCTA morphologic parameters associated with PCI outcome (P&lt;0.05). In addition, the blunt stump, occlusion length ≥20 mm, and ostial or bifurcation lesions, were the CCA morphologic parameters associated with PCI outcome (P&lt;0.05). The multivariate regression model showed that the three independent negative predictors: blunt stump on CCA (OR: 0.63; 95 % CI: 0.23–0.98; p =0.048), occlusion length ≥20 mm on CCTA (OR: 0.37; 95 % CI: 0.32–0.71; p &lt;0.001) and negative remodeling on CCTA (OR: 0.26; 95 % CI: 0.28–0.44; p &lt;0.001).</div></div><div><h3>Conclusion</h3><div>Our study demonstrated that combining CCTA and CCA morphologic characteristics could improve PCI outcome prediction in patients with CTO compared to CCTA morphologic features alone.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100616"},"PeriodicalIF":1.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746687","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":"Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction","authors":"Christer Ruff ,&nbsp;Paula Bombach ,&nbsp;Constantin Roder ,&nbsp;Eliane Weinbrenner ,&nbsp;Christoph Artzner ,&nbsp;Leonie Zerweck ,&nbsp;Frank Paulsen ,&nbsp;Till-Karsten Hauser ,&nbsp;Ulrike Ernemann ,&nbsp;Georg Gohla","doi":"10.1016/j.ejro.2024.100617","DOIUrl":"10.1016/j.ejro.2024.100617","url":null,"abstract":"<div><div><strong>Rationale and Objectives:</strong> Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions.</div></div><div><h3>Materials and Methods:</h3><div>This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0 standards were assessed.</div></div><div><h3>Results</h3><div>All DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p &lt; 0.001 for all) and revealed higher SNR and CNR values (p &lt; 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84 % from the neuroradiologist, 100 % from the neurosurgeon, 92 % from the neuro-oncologist, and 84 % from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142).</div></div><div><h3>Conclusion</h3><div>This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100617"},"PeriodicalIF":1.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883233","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":"Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography","authors":"Antonio López-Rueda ,&nbsp;María-Ángeles Rodríguez-Sánchez ,&nbsp;Elena Serrano ,&nbsp;Javier Moreno ,&nbsp;Alejandro Rodríguez ,&nbsp;Laura Llull ,&nbsp;Sergi Amaro ,&nbsp;Laura Oleaga","doi":"10.1016/j.ejro.2024.100618","DOIUrl":"10.1016/j.ejro.2024.100618","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).</div></div><div><h3>Methods</h3><div>Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0–1 to train different classifiers. The sample was split into 80–20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.</div></div><div><h3>Results</h3><div>RF, KNN, and SVM, with the \"DropOut+SelectKBest\" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).</div></div><div><h3>Conclusion</h3><div>Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the \"DropOut+SelectKBest\" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100618"},"PeriodicalIF":1.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756636","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":"Different radiomics models in predicting the malignant potential of small intestinal stromal tumors","authors":"Yuxin Xie ,&nbsp;Chongfeng Duan ,&nbsp;Xuzhe Zhou ,&nbsp;Xiaoming Zhou ,&nbsp;Qiulin Shao ,&nbsp;Xin Wang ,&nbsp;Shuai Zhang ,&nbsp;Fang Liu ,&nbsp;Zhenbo Sun ,&nbsp;Ruirui Zhao ,&nbsp;Gang Wang","doi":"10.1016/j.ejro.2024.100615","DOIUrl":"10.1016/j.ejro.2024.100615","url":null,"abstract":"<div><h3>Objectives</h3><div>To explore the feasibility of different radiomics models for predicting the malignant potential of small intestinal stromal tumors (SISTs), and to select the best radiomics model.</div></div><div><h3>Methods</h3><div>A retrospective analysis of 140 patients with SISTs was conducted. Radiomics features were extracted from CT-enhanced images. Support vector machine (SVM), Decision tree (DT), Conditional inference trees (CIT), Random Forest (RF), K-nearest neighbors (KNN), Back-propagation neural network (BPNet), and Bayes were used to construct different radiomics models. The clinical data and CT performance were selected using univariate analysis and to construct clinical model. Nomogram model was developed by combining clinical data and radiomics features. Model performances were assessed by using the area under the receiver operator characteristic (ROC) curve (AUC). The models’ clinical values were assessed by decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>A total of 1132 radiomics features were extracted. Among radiomics models, SVM was better than DT, CIT, RF, KNN, BPNet, Bayes because it had the highest AUC with a significant difference (P&lt;0.05). The AUC of the clinical model was 0.781. The AUC of the radiomics model was 0.910. The AUC of nomogram model was 0.938. Clinical models had the lowest AUC. Nomogram AUC were slightly higher than radiomics model, but the difference was not significant (P=0.48). The DCA of the nomogram model and radiomics model showed optimal clinical efficacy.</div></div><div><h3>Conclusions</h3><div>The model constructed with SVM method was the best model for predicting the malignant potential of SISTs. Radiomics model and nomogram model showed high predictive value in predicting the malignant potential of SISTs.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100615"},"PeriodicalIF":1.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701825","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":"Low-dose lung CT: Optimizing diagnostic radiation dose – A phantom study","authors":"Michael Zellner ,&nbsp;Sebastian Tschauner ,&nbsp;Mathias S. Weyland ,&nbsp;Peter Eggenberger Hotz ,&nbsp;Stephan Scheidegger ,&nbsp;Christian J. Kellenberger","doi":"10.1016/j.ejro.2024.100614","DOIUrl":"10.1016/j.ejro.2024.100614","url":null,"abstract":"<div><h3>Background/purpose</h3><div>To investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images.</div></div><div><h3>Methods</h3><div>Axial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings.</div></div><div><h3>Results</h3><div>Adequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA.</div></div><div><h3>Conclusions</h3><div>Determination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100614"},"PeriodicalIF":1.8,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701824","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":"Initial experience with Double-vein Embolization in Hungary","authors":"David Adam Korda ,&nbsp;Andras Bibok ,&nbsp;Attila Doros ,&nbsp;Denes Horvathy ,&nbsp;Oszkar Hahn ,&nbsp;Balint Kokas ,&nbsp;Damjan Pekli ,&nbsp;Anna Zsofia Meltzer ,&nbsp;Attila Szijarto ,&nbsp;Domonkos Nadasdy-Horvath ,&nbsp;Pal Akos Deak","doi":"10.1016/j.ejro.2024.100613","DOIUrl":"10.1016/j.ejro.2024.100613","url":null,"abstract":"<div><h3>Introduction</h3><div>In recent years several new techniques have emerged to induce hypertrophy of the future liver remnant prior to major hepatectomies. We aimed to summarize our initial experience with Double-vein Embolization as the first center in Hungary.</div></div><div><h3>Methods</h3><div>Between March 2023 and August 2024 a total of 16 Double-vein Embolization procedures were performed in Semmelweis University. Future liver remnant volume was calculated based on computed tomography scans obtained within 4 weeks prior and 2–3 weeks after the procedure. Tc-99m mebrofenin hepatobiliary scintigraphy results were available for 12/16 patients.</div></div><div><h3>Results</h3><div>Technical success rate was 100 %. No major complication was observed. Successful resection rate was 93.8 %. One patient died due to post-hepatectomy liver failure. Future liver remnant volume and ratio increased significantly after the procedure compared to baseline (433.1 ± 163.8 cm³ vs. 603.5 ± 201.8 cm³, p &lt; 0.0001 and 27.2 ± 6.5 % vs. 37 ± 8.8 %, p &lt; 0.0001, respectively). Future liver remnant clearance improved significantly 1 and 2 weeks after the procedure (1.68 ± 0.58 %/min/m² vs. 2.44 ± 0.64 %/min/m² and 2.39 ± 0.31 %/min/m², respectively). Mean function gain was 50.6 % after one week and 60.1% after two weeks, respectively.</div></div><div><h3>Discussion</h3><div>Volumetric and functional outcomes in the present study are comparable with results reported in the literature. Our findings provide further evidence that Double-vein Embolization is a safe procedure that offers sufficient volumetric and functional gain in most candidates for liver resection. However, further studies are needed to define the exact place of this new technique in clinical practice.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100613"},"PeriodicalIF":1.8,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701823","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}