Diagnostic and Interventional Imaging最新文献

{"title":"CT, MRI and PET/CT of adrenal schwannoma","authors":"Lalla Maria Yaacoubi, Martin Gaillard, Maxime Barat","doi":"10.1016/j.diii.2024.07.006","DOIUrl":"10.1016/j.diii.2024.07.006","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 10","pages":"Pages 407-408"},"PeriodicalIF":4.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876372","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 characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge","authors":"Theodore Aouad ,&nbsp;Valerie Laurent ,&nbsp;Paul Levant ,&nbsp;Agnes Rode ,&nbsp;Nina Brillat-Savarin ,&nbsp;Pénélope Gaillot ,&nbsp;Christine Hoeffel ,&nbsp;Eric Frampas ,&nbsp;Maxime Barat ,&nbsp;Roberta Russo ,&nbsp;Mathilde Wagner ,&nbsp;Magaly Zappa ,&nbsp;Olivier Ernst ,&nbsp;Anais Delagnes ,&nbsp;Quentin Fillias ,&nbsp;Lama Dawi ,&nbsp;Céline Savoye-Collet ,&nbsp;Pauline Copin ,&nbsp;Paul Calame ,&nbsp;Edouard Reizine ,&nbsp;Nathalie Lassau","doi":"10.1016/j.diii.2024.07.002","DOIUrl":"10.1016/j.diii.2024.07.002","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of the 2023 SFR data challenge was to invite researchers to develop artificial intelligence (AI) models to identify the presence of a pancreatic mass and distinguish between benign and malignant pancreatic masses on abdominal computed tomography (CT) examinations.</div></div><div><h3>Materials and methods</h3><div><span>Anonymized abdominal CT examinations acquired during the portal venous phase were collected from 18 French centers. Abdominal CT examinations were divided into three groups including CT examinations with no lesion, CT examinations with benign pancreatic mass, or CT examinations with malignant pancreatic mass. Each team included at least one radiologist, one data scientist, and one engineer. Pancreatic lesions were annotated by expert radiologists. CT examinations were distributed in balanced batches via a Health Data Hosting certified platform. Data were distributed into four batches, two for training, one for internal evaluation, and one for the external evaluation. Training used 83 % of the data from 14 centers and external evaluation used data from the other four centers. The metric (</span><em>i.e.</em>, final score) used to rank the participants was a weighted average of mean sensitivity, mean precision and mean area under the curve.</div></div><div><h3>Results</h3><div>A total of 1037 abdominal CT examinations were divided into two training sets (including 500 and 232 CT examinations), an internal evaluation set (including 139 CT examinations), and an external evaluation set (including 166 CT examinations). The training sets were distributed on September 7 and October 13, 2023, and evaluation sets on October 15, 2023. Ten teams with a total of 93 members participated to the data challenge, with the best final score being 0.72.</div></div><div><h3>Conclusion</h3><div>This SFR 2023 data challenge based on multicenter CT data suggests that the use of AI for pancreatic lesions detection is possible on real data, but the distinction between benign and malignant pancreatic lesions remains challenging.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 10","pages":"Pages 395-399"},"PeriodicalIF":4.9,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761823","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 role of MR imaging in ovarian tumor risk stratification","authors":"Laure Fournier","doi":"10.1016/j.diii.2024.07.001","DOIUrl":"10.1016/j.diii.2024.07.001","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 10","pages":"Pages 353-354"},"PeriodicalIF":4.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753064","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":"When artificial intelligence meets photon-counting coronary CT angiography to reduce the need for invasive coronary angiography in TAVR candidates","authors":"Farah Cadour,&nbsp;Jean-Nicolas Dacher","doi":"10.1016/j.diii.2024.02.007","DOIUrl":"10.1016/j.diii.2024.02.007","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 243-244"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984180","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":"4D flow cardiac MRI to assess pulmonary blood flow in patients with pulmonary arterial hypertension associated with congenital heart disease","authors":"Estibaliz Valdeolmillos ,&nbsp;Hichem Sakhi ,&nbsp;Marine Tortigue ,&nbsp;Marion Audié ,&nbsp;Marc-Antoine Isorni ,&nbsp;Florence Lecerf ,&nbsp;Olivier Sitbon ,&nbsp;David Montani ,&nbsp;Xavier Jais ,&nbsp;Laurent Savale ,&nbsp;Marc Humbert ,&nbsp;Arshid Azarine ,&nbsp;Sébastien Hascoët","doi":"10.1016/j.diii.2024.01.009","DOIUrl":"10.1016/j.diii.2024.01.009","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to evaluate the accuracy of four-dimensional flow cardiac magnetic resonance imaging (4D flow MRI) compared to right heart catheterization in measuring pulmonary flow (Qp), systemic flow (Qs) and pulmonary-to-systemic flow ratio (Qp/Qs) in patients with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD).</p></div><div><h3>Materials and methods</h3><p>The study was registered on Clinical-trial.gov (NCT03928002). Sixty-four patients with PAH-CHD who underwent 4D flow MRI were included. There were 16 men and 48 women with a mean age of 45.3 ± 13.7 (standard deviation [SD]) years (age range: 21–77 years). Fifty patients (50/64; 78%) presented with pre-tricuspid shunt. Qp (L/min), Qs (L/min) and Qp/Qs were measured invasively using direct Fick method during right heart catheterization and compared with measurements assessed by 4D flow MRI within a 24–48-hour window.</p></div><div><h3>Results</h3><p>The average mean pulmonary artery pressure was 51 ± 17 (SD) mm Hg with median pulmonary vascular resistance of 8.8 Wood units (Q1, Q3: 5.3, 11.7). A strong linear correlation was found between Qp measurements obtained with 4D flow MRI and those obtained with the Fick method (<em>r</em> = 0.96; <em>P</em> &lt; 0.001). Bland Altman analysis indicated a mean difference of 0.15 ± 0.48 (SD) L/min between Qp estimated by 4D flow MRI and by right heart catheterization. A strong correlation was found between Qs and Qp/Qs measured by 4D flow MRI and those obtained with the direct Fick method (<em>r</em> = 0.85 and <em>r</em> = 0.92; <em>P</em> &lt; 0.001 for both).</p></div><div><h3>Conclusion</h3><p>Qp as measured by 4D flow MRI shows a strong correlation with measurements derived from the direct Fick method. Further investigation is needed to develop less complex and standardized methods for measuring essential PAH parameters, such as pulmonary arterial pressures and pulmonary vascular resistance.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 266-272"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139898306","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":"More evidence to support greater use of 4D flow cardiac MRI","authors":"David A Bluemke ,&nbsp;Nadine Kawel-Boehm","doi":"10.1016/j.diii.2024.02.014","DOIUrl":"10.1016/j.diii.2024.02.014","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 245-246"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066020","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":"Improved image quality and abdominal lesion detection with photon-counting CT compared to dual-source CT: New evidence from a phantom study","authors":"","doi":"10.1016/j.diii.2024.06.008","DOIUrl":"10.1016/j.diii.2024.06.008","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 10","pages":"Pages 349-350"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141493942","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":"Dual-energy CT: Bridging the gap between innovation and clinical practice","authors":"Paul Calame ,&nbsp;Sébastien Mulé","doi":"10.1016/j.diii.2024.02.011","DOIUrl":"10.1016/j.diii.2024.02.011","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 247-248"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140068822","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 potential and pitfalls of ChatGPT in radiology","authors":"Augustin Lecler ,&nbsp;Philippe Soyer ,&nbsp;Bo Gong","doi":"10.1016/j.diii.2024.05.003","DOIUrl":"10.1016/j.diii.2024.05.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 249-250"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177184","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":"Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence","authors":"Jan M. Brendel ,&nbsp;Jonathan Walterspiel ,&nbsp;Florian Hagen ,&nbsp;Jens Kübler ,&nbsp;Jean-François Paul ,&nbsp;Konstantin Nikolaou ,&nbsp;Meinrad Gawaz ,&nbsp;Simon Greulich ,&nbsp;Patrick Krumm ,&nbsp;Moritz Winkelmann","doi":"10.1016/j.diii.2024.01.010","DOIUrl":"10.1016/j.diii.2024.01.010","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.</p></div><div><h3>Materials and methods</h3><p>Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.</p></div><div><h3>Results</h3><p>A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51–93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0–98.7), 68.7% (95% CI: 60.1–76.4), 74.3 % (95% CI: 69.1–78.8), 94.8% (95% CI: 88.5–97.8), and 81.9% (95% CI: 76.7–86.4) for PC-CCTA, and 96.8% (95% CI: 92.1–99.1), 87.3% (95% CI: 80.5–92.4), 87.8% (95% CI: 82.2–91.8), 96.7% (95% CI: 91.7–98.7), and 91.9% (95% CI: 87.9–94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88–0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77–0.87) (<em>P</em> &lt; 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) <em>vs.</em> 97 out of 260 (37.3%) using PC-CCTA alone (<em>P</em> &lt; 0.001).</p></div><div><h3>Conclusion</h3><p>Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 7","pages":"Pages 273-280"},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211568424000354/pdfft?md5=08a79564cba5d15db35d4e6c9db76424&pid=1-s2.0-S2211568424000354-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139898307","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}