European Journal of Radiology Open最新文献

{"title":"Insertional Achilles tendinopathy: A radiographic cross-sectional comparison between symptomatic and asymptomatic heel of 71 patients","authors":"Kenichiro Nakajima","doi":"10.1016/j.ejro.2024.100568","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100568","url":null,"abstract":"<div><h3>Purpose</h3><p>This retrospective study aimed to investigate whether the standard radiographic indicators for Haglund's syndrome are applicable to insertional Achilles tendinopathy.</p></div><div><h3>Methods</h3><p>Patients who underwent surgery for insertional Achilles tendinopathy in one heel and experienced no pain in the other heel were enrolled in this study. Preoperative calibrated radiographs of the lateral view of the calcaneus were assessed using (1) calcaneal pitch angle, (2) Fowler-Phillip angle, (3) posterior calcaneal angle, (4) Chauveau-Liet angle, (5) X/Y ratio, (6) Haglund’s deformity height, (7) Haglund’s deformity peak angle, (8) calcification length, (9) calcification width, (10) parallel pitch test, and (11) presence of free body. The Wilcoxon signed rank test and McNemar’s test were used for statistical analyses.</p></div><div><h3>Results</h3><p>Seventy-one patients (52 males; mean age, 57.2; mean body mass index, 27.1) were included. Mean values for each index in the symptomatic and asymptomatic heels were as follows, respectively: (1) 23.5, 23.0 (<em>p</em> = 0.30); (2) 58.9, 57.8 (<em>p</em> &lt; 0.05); (3) 7.6, 9.2 (<em>p</em> &lt; 0.05); (4) 15.8, 13.9 (<em>p</em> &lt; 0.05); (5) 2.8, 2.8 (<em>p</em> = 0.87); (6) 5.4, 5.0 (<em>p</em> &lt; 0.05); (7) 99.6, 99.0 (<em>p</em> = 0.44); (8) 10.5, 7.6 (<em>p</em> &lt; 0.001); and (9) 5.1, 4.4 (<em>p</em> &lt; 0.05). The sensitivity, specificity, and area under curve of significant indicators were as follows, respectively: (2) 0.78, 0.37, 0.55; (3) 0.45, 0.72, 0.58; (4) 0.63, 0.54, 0.57; (6) 0.45, 0.69, 0.59; (8) 0.48, 0.80, 0.66; and (9) 0.63, 0.54, 0.59. The presence of free body also showed a significant difference between both heels (<em>p</em> &lt; 0.05).</p></div><div><h3>Conclusion</h3><p>Some radiographic indicators for Haglund's syndrome are applicable to the diagnosis of insertional Achilles tendinopathy. A comparison of the parameters of Haglund’s syndrome with those of insertional Achilles tendinopathy may illuminate the etiology and pathology of insertional Achilles tendinopathy and lead to novel treatments.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100568"},"PeriodicalIF":2.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000236/pdfft?md5=2037a047a4b1c66f44d7296043e1cd05&pid=1-s2.0-S2352047724000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905921","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":"Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T – Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction","authors":"Felix Schlicht ,&nbsp;Jan Vosshenrich ,&nbsp;Ricardo Donners ,&nbsp;Alina Carolin Seifert ,&nbsp;Matthias Fenchel ,&nbsp;Dominik Nickel ,&nbsp;Markus Obmann ,&nbsp;Dorothee Harder ,&nbsp;Hanns-Christian Breit","doi":"10.1016/j.ejro.2024.100567","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100567","url":null,"abstract":"<div><h3>Objectives</h3><p>To evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time.</p></div><div><h3>Materials and methods</h3><p>Lumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences.</p></div><div><h3>Results</h3><p>The images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min.</p></div><div><h3>Conclusions</h3><p>Advanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times.</p></div><div><h3>Clinical relevance</h3><p>Advanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100567"},"PeriodicalIF":2.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000224/pdfft?md5=758cb01b85dcdca1cb917fb3cdfbb660&pid=1-s2.0-S2352047724000224-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813332","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":"Comparing 2-dimensional versus 3-dimensional MR myelography for cerebrospinal fluid leak detection","authors":"Iichiro Osawa ,&nbsp;Takashi Mitsufuji ,&nbsp;Keita Nagawa ,&nbsp;Yuki Hara ,&nbsp;Toshimasa Yamamoto ,&nbsp;Nobuo Araki ,&nbsp;Eito Kozawa","doi":"10.1016/j.ejro.2024.100565","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100565","url":null,"abstract":"<div><h3>Purpose</h3><p>We compared cerebrospinal fluid (CSF) leak conspicuity and image quality as visualized using 3D versus 2D magnetic resonance (MR) myelography in patients with spinal CSF leaks.</p></div><div><h3>Methods</h3><p>Eighteen patients underwent spinal MR imaging at 3 Tesla. Three board-certified radiologists independently evaluated CSF leak conspicuity and image quality on a 4-point scale; the latter assessed by scoring fat suppression, venous visualization, and severity of CSF flow artifacts. Additionally, the evaluators ranked the overall performances of 2D versus 3D MR myelography upon completing side-by-side comparisons of CSF leak conspicuity. Inter-reader agreement was determined using the Gwet’s AC1.</p></div><div><h3>Results</h3><p>The quality of 3D MR myelography images was significantly better than that of 2D MR myelography with respect to CSF leak conspicuity (mean scores: 3.3 vs. 1.9, <em>p</em> &lt; 0.0001) and severity of CSF flow artifacts on the axial view (mean scores: 1.0 vs. 2.5, <em>p</em> = 0.0001). Inter-reader agreement was moderate to almost perfect for 2D MR myelography (AC1 = 0.55–1.00), and almost perfect for 3D MR myelography (AC1 = 0.85–1.00). Moreover, 3D MR myelography was judged to be superior to 2D acquisition in 78 %, 83 %, and 83 % of the samples per readers 1, 2 and 3, respectively; the inter-reader agreement was almost perfect (AC1: reader 1 vs. 2; 0.98, reader 2 vs. 3; 0.96, reader 3 vs. 1; 0.98).</p></div><div><h3>Conclusion</h3><p>CSF leaks are more conspicuous when using 3D MR myelography than when using its 2D counterpart; therefore, the former is more reliable for identifying such leaks.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100565"},"PeriodicalIF":2.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000200/pdfft?md5=df7496bb249ecf4943a1c38acd785ecd&pid=1-s2.0-S2352047724000200-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643968","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":"Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review","authors":"Shirin Yaghoobpoor ,&nbsp;Mobina Fathi ,&nbsp;Hamed Ghorani ,&nbsp;Parya Valizadeh ,&nbsp;Payam Jannatdoust ,&nbsp;Arian Tavasol ,&nbsp;Melika Zarei ,&nbsp;Arvin Arian","doi":"10.1016/j.ejro.2024.100561","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100561","url":null,"abstract":"<div><h3>Background and objective</h3><p>Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound.</p></div><div><h3>Methods</h3><p>A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment.</p></div><div><h3>Results</h3><p>Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data.</p></div><div><h3>Conclusion</h3><p>This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100561"},"PeriodicalIF":2.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000169/pdfft?md5=ef912140f1c0c9888e41da85ac25a347&pid=1-s2.0-S2352047724000169-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643967","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 of different imaging modalities for splenic malignancies: A comparative meta-analysis","authors":"Parya Valizadeh ,&nbsp;Payam Jannatdoust ,&nbsp;Mohammadreza Tahamtan ,&nbsp;Hamed Ghorani ,&nbsp;Soroush Soleimani Dorcheh ,&nbsp;Khashayar Farnoud ,&nbsp;Faeze Salahshour","doi":"10.1016/j.ejro.2024.100566","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100566","url":null,"abstract":"<div><h3>Background and objectives</h3><p>The spleen hosts both benign and malignant lesions. Despite multiple imaging modalities, the distinction between these lesions poses a diagnostic challenge, marked by varying diagnostic accuracy levels across methods. In this study, we aimed to evaluate and compare the diagnostic performance of various imaging techniques for detecting malignant splenic lesions.</p></div><div><h3>Methods</h3><p>Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Sciences databases for studies evaluating imaging techniques in detecting malignant splenic lesions. Data extraction included diagnostic accuracy metrics, and methodological quality was assessed using QUADAS-2. Diagnostic Test Accuracy meta-analyses were conducted using R (version: 4.2.1). Subgroup analyses and meta-regression were performed to compare different modalities and clinical settings.</p></div><div><h3>Results</h3><p>Our study included 28 studies (pooled sample size: 2358), primarily using retrospective designs with histopathology as the reference standard. PET scan demonstrated the highest diagnostic accuracy (AUC: 92 %), demonstrating a sensitivity of 93 % (95 % CI: 80.4 % - 97.7 %) and a specificity of 82.8 % (95 % CI: 71.1 % - 90.4 %). Contrast-enhanced ultrasound (CEUS), Contrast-enhanced CT scan, and contrast-enhanced MRI also showed impressive performance with AUCs of 91.4 %, 90.9 %, and 85.3 %, respectively. Differences among these modalities were not statistically significant, but they outperformed non-contrast-enhanced methods. PET and CEUS exhibited higher specificity for lymphoma cases compared to studies including other malignancies.</p></div><div><h3>Conclusion and clinical implications</h3><p>Overall, PET emerges as the best modality for splenic malignancies, and CEUS and CE-MRI show promise as potential alternatives, notably due to their reduced radiation exposure. Further research is essential for precise malignancy differentiation.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100566"},"PeriodicalIF":2.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000212/pdfft?md5=abf75c25948df04dfa47eb2153f47843&pid=1-s2.0-S2352047724000212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140631639","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":"Narrow band imaging-based radiogenomics for predicting radiosensitivity in nasopharyngeal carcinoma","authors":"Cheng-Wei Tie ,&nbsp;Xin Dong ,&nbsp;Ji-Qing Zhu ,&nbsp;Kai Wang ,&nbsp;Xu-Dong Liu ,&nbsp;Yu-Meng Liu ,&nbsp;Gui-Qi Wang ,&nbsp;Ye Zhang ,&nbsp;Xiao-Guang Ni","doi":"10.1016/j.ejro.2024.100563","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100563","url":null,"abstract":"<div><h3>Objectives</h3><p>This study aims to assess the efficacy of narrow band imaging (NBI) endoscopy in utilizing radiomics for predicting radiosensitivity in nasopharyngeal carcinoma (NPC), and to explore the associated molecular mechanisms.</p></div><div><h3>Materials</h3><p>The study included 57 NPC patients who were pathologically diagnosed and underwent RNA sequencing. They were categorized into complete response (CR) and partial response (PR) groups after receiving radical concurrent chemoradiotherapy. We analyzed 267 NBI images using ResNet50 for feature extraction, obtaining 2048 radiomic features per image. Using Python for deep learning and least absolute shrinkage and selection operator for feature selection, we identified differentially expressed genes associated with radiomic features. Subsequently, we conducted enrichment analysis on these genes and validated their roles in the tumor immune microenvironment through single-cell RNA sequencing.</p></div><div><h3>Results</h3><p>After feature selection, 54 radiomic features were obtained. The machine learning algorithm constructed from these features showed that the random forest algorithm had the highest average accuracy rate of 0.909 and an area under the curve of 0.961. Correlation analysis identified 30 differential genes most closely associated with the radiomic features. Enrichment and immune infiltration analysis indicated that tumor-associated macrophages are closely related to treatment responses. Three key NBI differentially expressed immune genes (NBI-DEIGs), namely CCL8, SLC11A1, and PTGS2, were identified as regulators influencing treatment responses through macrophages.</p></div><div><h3>Conclusion</h3><p>NBI-based radiomics models introduce a novel and effective method for predicting radiosensitivity in NPC. The molecular mechanisms may involve the functional states of macrophages, as reflected by key regulatory genes.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100563"},"PeriodicalIF":2.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000182/pdfft?md5=d020dd4a8958452b6cb40c6c1b6f4ceb&pid=1-s2.0-S2352047724000182-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622316","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":"Modified respiratory-triggered SPACE sequences for magnetic resonance cholangiopancreatography","authors":"Dayong Jin ,&nbsp;Xin Li ,&nbsp;Yifan Qian,&nbsp;Yanqiang Qiao,&nbsp;Liyao Liu,&nbsp;Juan Tian,&nbsp;Lei Wang,&nbsp;Yongli Ma,&nbsp;Yue Qin,&nbsp;Yinhu Zhu","doi":"10.1016/j.ejro.2024.100564","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100564","url":null,"abstract":"<div><h3>Background</h3><p>Respiratory-triggered (RT) and breath-hold are the most common acquisition modalities for magnetic resonance cholangiopancreatography (MRCP). The present study compared the three different acquisition modalities for optimizing the use of MRCP in patients with diseases of the pancreatic and biliary systems.</p></div><div><h3>Materials and methods</h3><p>Three MRCP acquisition modalities were used in this study: conventional respiratory-triggered sampling perfection with application-optimized contrasts using different flip evolutions (RT-SPACE), modified RT-SPACE, and breath-hold (BH)-SPACE. Fifty-eight patients with clinically suspected pancreatic and biliary system disease were included. All image data were acquired on a 1.5 T MR. Scan time and image quality were compared between the three acquisition modalities. Friedman test, which was followed by post-hoc analysis, was performed among triple-scan protocol.</p></div><div><h3>Results</h3><p>There was a significant difference in the mean acquisition time among conventional RT-SPACE, modified RT-SPACE, and BH-SPACE (167.41±32.11 seconds vs 50.84±73.78 seconds vs 18.00 seconds, <em>P</em> &lt;0.001). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also significantly different among the three groups (<em>P</em> &lt;0.001). The SNR and CNR were higher in the RT-SPACE group than in the BH-SPACE group (<em>P</em> &lt;0.05). However, there were no statistically significant differences (<em>P</em> &gt;0.05) among the 3 groups regarding quality of overall image, image clarity, background inhibition, and visualization of the pancreatic and biliary system.</p></div><div><h3>Conclusions</h3><p>MRCP acquisition with the modified RT-SPACE sequence greatly shortens the acquisition time with comparable quality images. The MRCP acquisition modality could be designed based on the patient's situation to improve the examination pass rate and obtain excellent images for diagnosis.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100564"},"PeriodicalIF":2.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000194/pdfft?md5=8f0fdf9ade75eed9c21ff5242b0a3319&pid=1-s2.0-S2352047724000194-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622315","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":"Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation","authors":"Teresa M. Tareco Bucho ,&nbsp;Liliana Petrychenko ,&nbsp;Mohamed A. Abdelatty ,&nbsp;Nino Bogveradze ,&nbsp;Zuhir Bodalal ,&nbsp;Regina G.H. Beets-Tan ,&nbsp;Stefano Trebeschi","doi":"10.1016/j.ejro.2024.100562","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100562","url":null,"abstract":"<div><h3>Background</h3><p>The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability.</p></div><div><h3>Methods</h3><p>Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process.</p></div><div><h3>Results</h3><p>The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection.</p></div><div><h3>Conclusions</h3><p>Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100562"},"PeriodicalIF":2.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000170/pdfft?md5=af783b599123985e9f85386304718a03&pid=1-s2.0-S2352047724000170-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140558728","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 multi-parameter MRI in preoperative staging of endometrial carcinoma","authors":"Lianbi Zhang,&nbsp;Liqiong Liu","doi":"10.1016/j.ejro.2024.100559","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100559","url":null,"abstract":"<div><h3>Background</h3><p>Endometrial carcinoma (EC) is a prevalent gynecological malignancy, necessitating accurate preoperative staging for effective treatment planning. This study explores the application value of multi-parameter MRI in diagnosing and staging endometrial cancer.</p></div><div><h3>Methods</h3><p>Seventy-six patients diagnosed with endometrial cancer underwent 3.0 T pelvic MRI within two weeks before surgery. Imaging data were analyzed based on FIGO clinical staging criteria. The study assessed the sensitivity, specificity, positive predictive value, and negative predictive value of MRI for each stage.</p></div><div><h3>Results</h3><p>Postoperative pathology confirmed 71 cases of endometrial adenocarcinoma, 3 serous adenocarcinoma, and 2 clear cell carcinomas. MRI staging showed a high consistency (Kappa value = 0.786) with postoperative pathology. The overall accuracy of MRI diagnosis was 86.8%. Sensitivity and specificity varied for each stage: IA (91.3%, 96.2%), IB (88.6%, 93.8%), II (97.4%, 89.2%), and III (84.2%, 100%).</p></div><div><h3>Conclusion</h3><p>While there was a slight misdiagnosis rate, the overall accuracy of preoperative MRI for endometrial cancer was high, aiding in precise diagnosis and clinical staging. MRI effectively identified myometrial infiltration, cervical involvement, paracentral extension, and lymph node metastasis. Further research with larger sample sizes is recommended for enhanced reliability.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100559"},"PeriodicalIF":2.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000145/pdfft?md5=39c0677d377670f06a6c01dd5f958f31&pid=1-s2.0-S2352047724000145-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190897","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 approach to distinguish between benign and malignant soft tissue tumors on magnetic resonance imaging","authors":"Lei Xu ,&nbsp;Meng-Yue Wang ,&nbsp;Liang Qi ,&nbsp;Yue-Fen Zou ,&nbsp;WU Fei-Yun ,&nbsp;Xiu-Lan Sun","doi":"10.1016/j.ejro.2024.100555","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100555","url":null,"abstract":"<div><h3>Objective</h3><p>To build a radiomics signature based on MRI images and evaluate its capability for preoperatively identifying the benign and malignant Soft tissue neoplasms (STTs).</p></div><div><h3>Materials and methods</h3><p>193 patients (99 malignant STTs and 94 benign STTs) were at random segmented into a training cohort (69 malignant STTs and 65 benign STTs) and a validation cohort (30 malignant STTs and 29 benign STTs) with a portion of 7:3. Radiomics features were extracted from T2 with fat saturation and T1 with fat saturation and gadolinium contrast images. Radiomics signature was developed by the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver that operated characteristics curve (ROC) analysis was used to assess radiomics signature's prediction performance. Inner validation was performed on an autonomous cohort that contained 40 patients.</p></div><div><h3>Results</h3><p>A radiomics was developed by a total of 16 radiomics features (5 original shape features and 11 were wavelet features) achieved favorable predictive efficacy. Malignant STTs showed higher radiomics score than benign STTs in both training cohort and validation cohort. A good prediction performance was shown by the radiomics signature in both training cohorts and validation cohorts. The training cohorts and validation cohorts had an area under curves (AUCs) of 0.885 and 0.841, respectively.</p></div><div><h3>Conclusions</h3><p>A radiomics signature based on MRI images can be a trustworthy imaging biomarker for identification of the benign and malignant STTs, which could help guide treatment strategies.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"12 ","pages":"Article 100555"},"PeriodicalIF":2.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000108/pdfft?md5=d88795b665e21521e75a472abe69cd32&pid=1-s2.0-S2352047724000108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181334","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}