Diagnostic and Interventional Imaging最新文献

{"title":"Treatment of adenomyosis, abdominal wall endometriosis and uterine leiomyoma with interventional radiology: A review of current evidences","authors":"Maxime Barat ,&nbsp;Anthony Dohan ,&nbsp;Maureen Kohi ,&nbsp;Clement Marcelin ,&nbsp;Jean-Pierre Pelage ,&nbsp;Alban Denys ,&nbsp;Sebastian Mafeld ,&nbsp;Claire S. Kaufman ,&nbsp;Philippe Soyer ,&nbsp;Francois H. Cornelis","doi":"10.1016/j.diii.2023.11.005","DOIUrl":"10.1016/j.diii.2023.11.005","url":null,"abstract":"<div><p><span>Interventional radiology shows promises in the field of women's health, particularly in pelvic interventions. This review article discusses the latest advancements in interventional radiology techniques for pelvic conditions affecting women including adenomyosis, abdominal wall </span>endometriosis and uterine leiomyoma. Extraperitoneal endometriosis involving the abdominal wall may be treated by percutaneous thermal ablation, such as cryoablation, whereas uterine leiomyoma and adenomyosis can be managed either using percutaneous thermal ablation or using uterine artery embolization. Continued research and development in interventional radiology will further enhance the minimally-invasive interventions available for women's health, improving outcomes and quality of life for this large patient population of women.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138555615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cardiac CT imaging quality: Precision metrics for assessing image quality for AI-powered reconstructions","authors":"Benjamin Longère,&nbsp;Jean-Nicolas Dacher","doi":"10.1016/j.diii.2023.11.004","DOIUrl":"10.1016/j.diii.2023.11.004","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138488805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI is indeed helpful but it should always be monitored!","authors":"Ali Guermazi","doi":"10.1016/j.diii.2024.02.013","DOIUrl":"https://doi.org/10.1016/j.diii.2024.02.013","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140041686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External validation of an artificial intelligence solution for the detection of elbow fractures and joint effusions in children","authors":"Michel Dupuis ,&nbsp;Léo Delbos ,&nbsp;Alexandra Rouquette ,&nbsp;Catherine Adamsbaum ,&nbsp;Raphaël Veil","doi":"10.1016/j.diii.2023.09.008","DOIUrl":"10.1016/j.diii.2023.09.008","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to conduct an external validation of an artificial intelligence (AI) solution for the detection of elbow fractures and joint effusions using radiographs from a real-life cohort of children.</p></div><div><h3>Materials and methods</h3><p>This single-center retrospective study was conducted on 758 radiographic sets (1637 images) obtained from consecutive emergency room visits of 712 children (mean age, 7.27 ± 3.97 [standard deviation] years; age range, 7 months and 10 days to 15 years and 10 months), referred for a trauma of the elbow. For each set, fracture and/or effusion detection by eleven senior radiologists (reference standard) and AI solution was recorded. Diagnostic performance of the AI solution was measured via four different approaches: fracture detection (presence/absence of fracture as binary variable), fracture enumeration, fracture localization and lesion detection (fracture and/or a joint effusion used as constructed binary variable).</p></div><div><h3>Results</h3><p>The sensitivity of the AI solution for each of the four approaches was &gt;89%. Greatest sensitivity of the AI solution was obtained for lesion detection (95.0%; 95% confidence interval: 92.1–96.9). The specificity of the AI solution ranged between 63% (for lesion detection) and 77% (for fracture detection). For all four approaches, the negative predictive values were &gt;92% and the positive predictive values ranged between 54% (for fracture enumeration and localization) and 73% (for lesion detection). Specificity was lower for plastered children for all approaches (<em>P</em> &lt; 0.001).</p></div><div><h3>Conclusion</h3><p>The AI solution demonstrates high performances for detecting elbow's fracture and/or joint effusion in children. However, in our context of use, 8% of the radiographic sets ruled-out by the algorithm concerned children with a genuine traumatic elbow lesion.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41183960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution","authors":"Aissam Djahnine ,&nbsp;Carole Lazarus ,&nbsp;Mathieu Lederlin ,&nbsp;Sébastien Mulé ,&nbsp;Rafael Wiemker ,&nbsp;Salim Si-Mohamed ,&nbsp;Emilien Jupin-Delevaux ,&nbsp;Olivier Nempont ,&nbsp;Youssef Skandarani ,&nbsp;Mathieu De Craene ,&nbsp;Segbedji Goubalan ,&nbsp;Caroline Raynaud ,&nbsp;Younes Belkouchi ,&nbsp;Amira Ben Afia ,&nbsp;Clement Fabre ,&nbsp;Gilbert Ferretti ,&nbsp;Constance De Margerie ,&nbsp;Pierre Berge ,&nbsp;Renan Liberge ,&nbsp;Nicolas Elbaz ,&nbsp;Loic Boussel","doi":"10.1016/j.diii.2023.09.006","DOIUrl":"10.1016/j.diii.2023.09.006","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations.</p></div><div><h3>Materials and methods</h3><p>Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (<em>i</em>), detecting blood clots; (<em>ii</em>), performing PE-positive versus negative classification; (<em>iii</em>), estimating the Qanadli score; and (<em>iv</em>), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio.</p></div><div><h3>Results</h3><p>Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850–0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810–0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668–0.760) and of 0.723 (95% CI: 0.668–0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set.</p></div><div><h3>Conclusion</h3><p>This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135706020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prostate artery embolization using liquid embolic agents: Is it the future or just a trend?","authors":"Tom Boeken","doi":"10.1016/j.diii.2024.02.003","DOIUrl":"10.1016/j.diii.2024.02.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifocal pseudomyogenic hemangioendothelioma: A misleading sarcoma-like tumor","authors":"Marie-Pauline Talabard ,&nbsp;Antoine Feydy","doi":"10.1016/j.diii.2024.02.008","DOIUrl":"10.1016/j.diii.2024.02.008","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139933667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)","authors":"Daphné Guenoun ,&nbsp;Marc Zins ,&nbsp;Pierre Champsaur ,&nbsp;Isabelle Thomassin-Naggara ,&nbsp;DRIM France AI Study Group","doi":"10.1016/j.diii.2023.09.002","DOIUrl":"10.1016/j.diii.2023.09.002","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.</p></div><div><h3>Materials and methods</h3><p><span>The RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, </span>template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting.</p></div><div><h3>Results</h3><p>The panel consisted of seven software vendors, three for bone fracture detection using conventional radiology<span> and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool.</span></p></div><div><h3>Conclusion</h3><p>The study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The AI “Grid”: A French national initiative as a product of radiology and industry collaboration","authors":"Bo Gong,&nbsp;Steven P. Rowe,&nbsp;Loic Duron","doi":"10.1016/j.diii.2023.10.001","DOIUrl":"10.1016/j.diii.2023.10.001","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50163309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intraprocedural assessment of ablation margins using computed tomography co-registration in hepatocellular carcinoma treatment with percutaneous ablation: IAMCOMPLETE study","authors":"Pim Hendriks ,&nbsp;Kiki M van Dijk ,&nbsp;Bas Boekestijn ,&nbsp;Alexander Broersen ,&nbsp;Jacoba J van Duijn-de Vreugd ,&nbsp;Minneke J Coenraad ,&nbsp;Maarten E Tushuizen ,&nbsp;Arian R van Erkel ,&nbsp;Rutger W van der Meer ,&nbsp;Catharina SP van Rijswijk ,&nbsp;Jouke Dijkstra ,&nbsp;Lioe-Fee de Geus-Oei ,&nbsp;Mark C Burgmans","doi":"10.1016/j.diii.2023.07.002","DOIUrl":"10.1016/j.diii.2023.07.002","url":null,"abstract":"<div><h3>Purpose</h3><p>The primary objective of this study was to determine the feasibility of ablation margin quantification using a standardized scanning protocol during thermal ablation (TA) of hepatocellular carcinoma (HCC), and a rigid registration algorithm. Secondary objectives were to determine the inter- and intra-observer variability of tumor segmentation and quantification of the minimal ablation margin (MAM).</p></div><div><h3>Materials and methods</h3><p>Twenty patients who underwent thermal ablation for HCC were included. There were thirteen men and seven women with a mean age of 67.1 ± 10.8 (standard deviation [SD]) years (age range: 49.1–81.1 years). All patients underwent contrast-enhanced computed tomography examination under general anesthesia directly before and after TA, with preoxygenated breath hold. Contrast-enhanced computed tomography examinations were analyzed by radiologists using rigid registration software. Registration was deemed feasible when accurate rigid co-registration could be obtained. Inter- and intra-observer rates of tumor segmentation and MAM quantification were calculated. MAM values were correlated with local tumor progression (LTP) after one year of follow-up.</p></div><div><h3>Results</h3><p>Co-registration of pre- and post-ablation images was feasible in 16 out of 20 patients (80%) and 26 out of 31 tumors (84%). Mean Dice similarity coefficient for inter- and intra-observer variability of tumor segmentation were 0.815 and 0.830, respectively. Mean MAM was 0.63 ± 3.589 (SD) mm (range: -6.26–6.65 mm). LTP occurred in four out of 20 patients (20%). The mean MAM value for patients who developed LTP was -4.00 mm, as compared to 0.727 mm for patients who did not develop LTP.</p></div><div><h3>Conclusion</h3><p>Ablation margin quantification is feasible using a standardized contrast-enhanced computed tomography protocol. Interpretation of MAM was hampered by the occurrence of tissue shrinkage during TA. Further validation in a larger cohort should lead to meaningful cut-off values for technical success of TA.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221156842300150X/pdfft?md5=5446a9590ead917701293c4bd42978aa&pid=1-s2.0-S221156842300150X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9951824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}