European Journal of Radiology Open最新文献

{"title":"Diagnostic accuracy and added value of dynamic chest radiography in detecting pulmonary embolism: A retrospective study","authors":"Yuzo Yamasaki ,&nbsp;Kazuya Hosokawa ,&nbsp;Takeshi Kamitani ,&nbsp;Kohtaro Abe ,&nbsp;Koji Sagiyama ,&nbsp;Takuya Hino ,&nbsp;Megumi Ikeda ,&nbsp;Shunsuke Nishimura ,&nbsp;Hiroyuki Toyoda ,&nbsp;Shohei Moriyama ,&nbsp;Masateru Kawakubo ,&nbsp;Noritsugu Matsutani ,&nbsp;Hidetake Yabuuchi ,&nbsp;Kousei Ishigami","doi":"10.1016/j.ejro.2024.100602","DOIUrl":"10.1016/j.ejro.2024.100602","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to assess the diagnostic performance of dynamic chest radiography (DCR) and investigate its added value to chest radiography (CR) in detecting pulmonary embolism (PE).</div></div><div><h3>Methods</h3><div>Of 775 patients who underwent CR and DCR in our hospital between June 2020 and August 2022, individuals who also underwent contrast-enhanced CT (CECT) of the chest within 72 h were included in this study. PE or non-PE diagnosis was confirmed by CECT and the subsequent clinical course. The enrolled patients were randomized into two groups. Six observers, including two thoracic radiologists, two cardiologists, and two radiology residents, interpreted each chest radiograph with and without DCR using a crossover design with a washout period. Diagnostic performance was compared between CR with and without DCR in the standing and supine positions.</div></div><div><h3>Results</h3><div>Sixty patients (15 PE, 45 non-PE) were retrospectively enrolled. The addition of DCR to CR significantly improved the sensitivity, specificity, accuracy, and area under the curve (AUC) in the standing (35.6–70.0 % [<em>P</em> &lt; 0.0001], 84.8–93.3 % [<em>P</em> = 0.0010], 72.5–87.5 % [<em>P</em> &lt; 0.0001], and 0.66–0.85 [<em>P</em> &lt; 0.0001], respectively) and supine (33.3–65.6 % [<em>P</em> &lt; 0.0001], 78.5–92.2 % [<em>P</em> &lt; 0.0001], 67.2–85.6 % [<em>P</em> &lt; 0.0001], and 0.62–0.80 [<em>P</em> = 0.0002], respectively) positions for PE detection. No significant differences were found between the AUC values of DCR with CR in the standing and supine positions (P = 0.11) or among radiologists, cardiologists, and radiology residents (P = 0.14–0.68).</div></div><div><h3>Conclusions</h3><div>Incorporating DCR with CR demonstrated moderate sensitivity, high specificity, and high accuracy in detecting PE, all of which were significantly higher than those achieved with CR alone, regardless of scan position, observer expertise, or experience.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100602"},"PeriodicalIF":1.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420066","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":"Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer","authors":"Anass Benfares ,&nbsp;Abdelali yahya Mourabiti ,&nbsp;Badreddine Alami ,&nbsp;Sara Boukansa ,&nbsp;Nizar El Bouardi ,&nbsp;Moulay Youssef Alaoui Lamrani ,&nbsp;Hind El Fatimi ,&nbsp;Bouchra Amara ,&nbsp;Mounia Serraj ,&nbsp;Smahi Mohammed ,&nbsp;Cherkaoui Abdeljabbar ,&nbsp;El affar Anass ,&nbsp;Mamoun Qjidaa ,&nbsp;Mustapha Maaroufi ,&nbsp;Ouazzani Jamil Mohammed ,&nbsp;Qjidaa Hassan","doi":"10.1016/j.ejro.2024.100601","DOIUrl":"10.1016/j.ejro.2024.100601","url":null,"abstract":"<div><h3>Purpose</h3><p>To propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer.</p></div><div><h3>Materials and methods</h3><p>Real-world data from 521 patients with adenocarcinoma NSCLC who performed a CT scan and underwent surgery or pathological biopsy to determine EGFR gene mutation between January 2021 and July 2022, is collected. Solutions to the problems that prevent the model from achieving very high precision, namely: human errors made during the annotation of the database and the low precision of the output decision of the model, are proposed. Thus, among the 521 analyzed cases, only 40 were selected as patients with EGFR gene mutation and 98 cases with wild-type EGFR.</p></div><div><h3>Results</h3><p>The proposed model is trained, validated, and tested on 12,040 2D images extracted from the 138 CT scans images where patients were randomly partitioned into training (80 %) and test (20 %) sets. The performance obtained for EGFR gene mutation prediction was 95.22 % for accuracy, 960.2 for F1_score, 95.89 % for precision, 96.92 % for sensitivity, 94.01 % for Cohen kappa, and 98 % for AUC.</p></div><div><h3>Conclusion</h3><p>An EGFR gene mutation status prediction method, with high-performance thanks to an intelligent prediction model entrained by highly accurate annotated data is proposed. The outcome of this project will facilitate rapid decision-making when applying a TKI as an initial treatment.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100601"},"PeriodicalIF":1.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235204772400056X/pdfft?md5=6b569d6b0991ebec79c5235f88184fd5&pid=1-s2.0-S235204772400056X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271651","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":"The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA","authors":"Jinxin Chen,&nbsp;Xinyi Zeng,&nbsp;Feng Li,&nbsp;Jidong Peng","doi":"10.1016/j.ejro.2024.100600","DOIUrl":"10.1016/j.ejro.2024.100600","url":null,"abstract":"<div><h3>Objective</h3><p>This study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA).</p></div><div><h3>Methods</h3><p>A group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques.</p></div><div><h3>Results</h3><p>Significant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P&lt;0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of −445.45 HU, a 3D density of 0.56 mg·mm⁻³, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881).</p></div><div><h3>Conclusion</h3><p>The utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100600"},"PeriodicalIF":1.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000558/pdfft?md5=6ba180c2567cfc76febca8a162b97b4b&pid=1-s2.0-S2352047724000558-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271652","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":"IMPeTUs parameters correlate with clinical features in newly diagnosed multiple myeloma","authors":"Shuaishuai Xu,&nbsp;Shengxiu Jiao,&nbsp;Huimin Guo,&nbsp;Wenkun Chen,&nbsp;Shuzhan Yao","doi":"10.1016/j.ejro.2024.100598","DOIUrl":"10.1016/j.ejro.2024.100598","url":null,"abstract":"<div><h3>Objectives</h3><p>To investigate the correlations between IMPeTUs-based 18 F-FDG PET/CT parameters and clinical features in patients with newly diagnosed multiple myeloma (MM).</p></div><div><h3>Materials and methods</h3><p>PET/CT were analysed according to the IMPeTUs criteria. We correlated these PET/CT parameters with known clinically relevant features, bone marrow plasma cell (BMPC) infiltration rate and the presence of cytogenetic abnormalities.</p></div><div><h3>Results</h3><p>A total of 149 patients (86 males, 63 females; mean age, 59.9 ± 9.7 years) were included. Bone marrow metabolic state correlated with the most clinical features including hemoglobin (rho=-0.23, p=0.004), FLC ratio (rho=0.24, p=0.005), β2 M (rho=0.28, p=0.001), CRP (rho=0.25, p=0.003), serum calcium (rho=0.22, p=0.02), serum creatinine (rho=0.24, p=0.004) and BMPC (rho=0.21, p=0.003). Besides, the level of hemoglobin was significant lower (0.043), and the levels of FLC ratio (0.037), β2 M (p=0.024), CRP (p=0.05), and BMPC (p=0.043) were significant higher in patients having hypermetabolism in limbs and ribs. Hottest bone lesion Deauville criteria had a moderate correlation with CRP (rho=0.27, p=0.001) and serum calcium (rho=0.25, p=0.01).</p></div><div><h3>Conclusion</h3><p>Several IMPeTUs-based PET/CT parameters showed significant correlations with clinical features reflecting disease burden and biology, suggesting that these new criteria can be used in the risk stratification in MM patients.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100598"},"PeriodicalIF":1.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000534/pdfft?md5=4846d3531257414e8fae9e95bd445ebb&pid=1-s2.0-S2352047724000534-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129131","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":"Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach","authors":"Elizabeth P.V. Le ,&nbsp;Mark Y.Z. Wong ,&nbsp;Leonardo Rundo ,&nbsp;Jason M. Tarkin ,&nbsp;Nicholas R. Evans ,&nbsp;Jonathan R. Weir-McCall ,&nbsp;Mohammed M. Chowdhury ,&nbsp;Patrick A. Coughlin ,&nbsp;Holly Pavey ,&nbsp;Fulvio Zaccagna ,&nbsp;Chris Wall ,&nbsp;Rouchelle Sriranjan ,&nbsp;Andrej Corovic ,&nbsp;Yuan Huang ,&nbsp;Elizabeth A. Warburton ,&nbsp;Evis Sala ,&nbsp;Michael Roberts ,&nbsp;Carola-Bibiane Schönlieb ,&nbsp;James H.F. Rudd","doi":"10.1016/j.ejro.2024.100594","DOIUrl":"10.1016/j.ejro.2024.100594","url":null,"abstract":"<div><h3>Purpose</h3><p>To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score.</p></div><div><h3>Methods</h3><p>Carotid CT angiography (CTA) images from symptomatic patients (ischaemic stroke/transient ischaemic attack within the last 3 months) and asymptomatic patients were analysed. Carotid arteries were classified into culprit, non-culprit and asymptomatic. The calcium score was assessed using the Agatston method. 93 radiomic features were extracted from regions-of-interest drawn on 14 consecutive CTA slices. For DL, convolutional neural networks (CNNs) with and without transfer learning were trained directly on CTA slices. Predictive performance was assessed over 5-fold cross validated AUC scores. SHAP and GRAD-CAM algorithms were used for explainability.</p></div><div><h3>Results</h3><p>132 carotid arteries were analysed (41 culprit, 41 non-culprit, and 50 asymptomatic). For asymptomatic vs symptomatic arteries, radiomics attained a mean AUC of 0.96(± 0.02), followed by DL 0.86(± 0.06) and then calcium 0.79(± 0.08). For culprit vs non-culprit arteries, radiomics achieved a mean AUC of 0.75(± 0.09), followed by DL 0.67(± 0.10) and then calcium 0.60(± 0.02). For multi-class classification, the mean AUCs were 0.95(± 0.07), 0.79(± 0.05), and 0.71(± 0.07) for radiomics, DL and calcium, respectively. Explainability revealed consistent patterns in the most important radiomic features.</p></div><div><h3>Conclusions</h3><p>Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100594"},"PeriodicalIF":1.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000492/pdfft?md5=bb89145f8b5e821a8f445782d782898c&pid=1-s2.0-S2352047724000492-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096934","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":"Impact of fat content on lumbar spine DWI performance: A sex-based comparative study","authors":"Liang Hu ,&nbsp;Jiang-Feng Pan ,&nbsp;Zheng Han,&nbsp;Xiu-Mei Xia","doi":"10.1016/j.ejro.2024.100597","DOIUrl":"10.1016/j.ejro.2024.100597","url":null,"abstract":"<div><h3>Purpose</h3><p>Sex-based differences in lumbar spine's fat content in adults are minimal, but significant variations exist in diffusion-weighted imaging (DWI) signal characteristics. This study aimed to investigate fat content’s impact on DWI performance in lumbar spine and potential sex differences.</p></div><div><h3>Methods</h3><p>A retrospective analysis was conducted on upper abdominal MRI examinations in asymptomatic adult. The lumbar 1 vertebral apparent diffusion coefficient (ADC) values and fat fraction were measured. Using DWI images (b = 800 s/mm²), the lumbar 1 vertebral signal was categorized into high and iso-low signal groups. A univariate and multivariate analysis was conducted to investigate the influence of fat fraction on DWI performance. Finally, the participants were divided into three groups to analyze sex differences in the effect of fat content on DWI performance.</p></div><div><h3>Results</h3><p>202 subjects, 99 men were included. Fat content significantly influenced lumbar spine DWI signal in both sexes (<em>p</em> &lt; 0.05). The effect on ADC values was significant only in women (<em>p</em> &lt; 0.001). Women demonstrated a significantly higher proportion of high DWI signal than men in the low (<em>p</em> = 0.002) and middle (<em>p</em> = 0.012) fat content groups. Additionally, women had higher ADC values in the low fat group (<em>p</em> = 0.004) but lower values in the high fat group (<em>p</em> = 0.004).</p></div><div><h3>Conclusion</h3><p>Fat content significantly impacts the DWI signal of lumbar spine, with a slight sex difference observed. These sex differences suggest that DWI signals may provide valuable information about the bone marrow beyond fat content.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100597"},"PeriodicalIF":1.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000522/pdfft?md5=85475f718a6604cd257db158d1c5e727&pid=1-s2.0-S2352047724000522-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096933","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":"The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones","authors":"Hong Zhu ,&nbsp;Deyan Kong ,&nbsp;Jiale Qian ,&nbsp;Xiaomeng Shi ,&nbsp;Jing Fan","doi":"10.1016/j.ejro.2024.100599","DOIUrl":"10.1016/j.ejro.2024.100599","url":null,"abstract":"<div><h3>Purpose</h3><p>To compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU).</p></div><div><h3>Methods</h3><p>A retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility.</p></div><div><h3>Results</h3><p>DH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P &gt; 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p &lt; 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P &gt; 0.05).</p></div><div><h3>Conclusion</h3><p>The DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.</p><p>The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100599"},"PeriodicalIF":1.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000546/pdfft?md5=26733059cc2a262840a0fc61adcdcfbb&pid=1-s2.0-S2352047724000546-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096932","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":"Radiomics on slice-reduced versus full-chest computed tomography for diagnosis and staging of interstitial lung disease in systemic sclerosis: A comparative analysis","authors":"Anja A. Joye ,&nbsp;Marta Bogowicz ,&nbsp;Janine Gote-Schniering ,&nbsp;Thomas Frauenfelder ,&nbsp;Matthias Guckenberger ,&nbsp;Britta Maurer ,&nbsp;Stephanie Tanadini-Lang ,&nbsp;Hubert S. Gabryś","doi":"10.1016/j.ejro.2024.100596","DOIUrl":"10.1016/j.ejro.2024.100596","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to evaluate the efficacy of radiomics derived from slice-reduced CT (srCT) scans versus full-chest CT (fcCT) for diagnosing and staging of interstitial lung disease (ILD) in systemic sclerosis (SSc), considering the potential to reduce radiation exposure.</p></div><div><h3>Material and methods</h3><p>The fcCT corresponded to a standard high-resolution full-chest CT whereas the srCT consisted of nine axial slices. 1451 radiomic features in two dimensions from srCT and 1375 features in three dimensions from fcCT scans were extracted from 166 SSc patients. The study included first- and second-order features from original and wavelet-transformed images. We assessed the predictive performance of quantitative CT (qCT)-based logistic regression (LR) models relying on preselected features and machine learning workflows involving LR and extra-trees classifiers with data-driven feature selection. The area under the receiver operating characteristic curve (AUC) was used to estimate model performance.</p></div><div><h3>Results</h3><p>The best models for diagnosis and staging ILD achieved AUC=0.85±0.08 and AUC=0.82±0.08 with srCT, and AUC=0.83±0.06 and AUC=0.76±0.08 with fcCT, respectively. srCT-based models showed slightly superior performance over fcCT-based models, particularly in 2D-radiomic analyses when interpolation resolution closely matched the original in-plane resolution. For diagnosis, the LR outperformed qCT-models, whereas for staging, the best results were obtained with a qCT-based model.</p></div><div><h3>Conclusions</h3><p>Radiomics from srCT is an effective and preferable alternative to fcCT for diagnosing and staging SSc-ILD. This approach not only enhances predictive accuracy but also minimizes radiation exposure risks, offering a promising avenue for improved treatment decision support in SSc-ILD management.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100596"},"PeriodicalIF":1.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000510/pdfft?md5=d4f83a55c0d66a111429abfa78cf9995&pid=1-s2.0-S2352047724000510-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096931","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":"Primary sclerosing cholangitis: Is qualitative and quantitative 3 T MR imaging useful for the evaluation of disease severity?","authors":"Piero Boraschi ,&nbsp;Valentina Mazzantini ,&nbsp;Francescamaria Donati ,&nbsp;Barbara Coco ,&nbsp;Barbara Vianello ,&nbsp;Andrea Pinna ,&nbsp;Riccardo Morganti ,&nbsp;Piero Colombatto ,&nbsp;Maurizia Rossana Brunetto ,&nbsp;Emanuele Neri","doi":"10.1016/j.ejro.2024.100595","DOIUrl":"10.1016/j.ejro.2024.100595","url":null,"abstract":"<div><h3>Purpose</h3><p>To analyze the role of qualitative and quantitative 3 T MR imaging assessment as a non-invasive method for the evaluation of disease severity in patients with primary sclerosing cholangitis (PSC).</p></div><div><h3>Methods</h3><p>A series of 26 patients, with histological diagnosis of PSC undergoing 3 T MRI and hepatological evaluation, was retrospectively enrolled. All MR examinations included diffusion-weighted imaging (DWI), T2-weighted (T2w) and T1-weighted (T1w) sequences, before and after administration of Gd-EOB-DTPA with the acquisition of both dynamic and hepato-biliary phase (HBP). Qualitative analysis was performed by assessment of liver parenchyma and biliary tract changes, also including biliary excretion of gadoxetic acid on HBP. Quantitative evaluation was conducted on liver parenchyma by measurement of apparent diffusion coefficient (ADC) and relative enhancement (RE) on 3-minute delayed phase and on HBP. Results of blood tests (ALT, ALP, GGT, total and direct bilirubin, albumin, and platelets) and transient elastography-derived liver stiffness measurements (TE-LSM) were collected and correlated with qualitative and quantitative MRI findings.</p></div><div><h3>Results</h3><p>Among qualitative and quantitative findings, fibrosis visual assessment and RE had the best performance in estimating disease severity, showing a statistically significant correlation with both biomarkers of cholestasis and TE-LSM. Statistical analysis also revealed a significant correlation of gadoxetic acid biliary excretion with ALT and direct bilirubin, as well as of ADC with total bilirubin.</p></div><div><h3>Conclusion</h3><p>Qualitative and quantitative 3 T MR evaluation is a promising non-invasive method for the assessment of disease severity in patients with PSC.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100595"},"PeriodicalIF":1.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000509/pdfft?md5=0e01005ca24f98d2928c6cb7a9ad3e17&pid=1-s2.0-S2352047724000509-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954097","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 for tubes and lines detection in critical illness: Generalizability and comparison with residents","authors":"Pootipong Wongveerasin,&nbsp;Trongtum Tongdee,&nbsp;Pairash Saiviroonporn","doi":"10.1016/j.ejro.2024.100593","DOIUrl":"10.1016/j.ejro.2024.100593","url":null,"abstract":"<div><h3>Background</h3><p>Artificial intelligence (AI) has been proven useful for the assessment of tubes and lines on chest radiographs of general patients. However, validation on intensive care unit (ICU) patients remains imperative.</p></div><div><h3>Methods</h3><p>This retrospective case-control study evaluated the performance of deep learning (DL) models for tubes and lines classification on both an external public dataset and a local dataset comprising 303 films randomly sampled from the ICU database. The endotracheal tubes (ETTs), central venous catheters (CVCs), and nasogastric tubes (NGTs) were classified into “Normal,” “Abnormal,” or “Borderline” positions by DL models with and without rule-based modification. Their performance was evaluated using an experienced radiologist as the standard reference.</p></div><div><h3>Results</h3><p>The algorithm showed decreased performance on the local ICU dataset, compared to that of the external dataset, decreasing from the Area Under the Curve of Receiver (AUC) of 0.967 (95 % CI 0.965–0.973) to the AUC of 0.70 (95 % CI 0.68–0.77). Significant improvement in the ETT classification task was observed after modifications were made to the model to allow the use of the spatial relationship between line tips and reference anatomy with the improvement of the AUC, increasing from 0.71 (95 % CI 0.70 – 0.75) to 0.86 (95 % CI 0.83 – 0.94)</p></div><div><h3>Conclusions</h3><p>The externally trained model exhibited limited generalizability on the local ICU dataset. Therefore, evaluating the performance of externally trained AI before integrating it into critical care routine is crucial. Rule-based algorithm may be used in combination with DL to improve results.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100593"},"PeriodicalIF":1.8,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000480/pdfft?md5=e3984dd26f8e8aa3a7cf367184907496&pid=1-s2.0-S2352047724000480-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962138","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}