Lucas de Pádua Gomes de Farias, Cesar Higa Nomura, Marcio Valente Yamada Sawamura
{"title":"Vanishing Cystic Air Spaces.","authors":"Lucas de Pádua Gomes de Farias, Cesar Higa Nomura, Marcio Valente Yamada Sawamura","doi":"10.1148/ryct.230200","DOIUrl":"10.1148/ryct.230200","url":null,"abstract":"","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"5 6","pages":"e230200"},"PeriodicalIF":7.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080902","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}
Judith van der Bie, Simran P Sharma, Marcel van Straten, Daniel Bos, Alexander Hirsch, Marcel L Dijkshoorn, Rik Adrichem, Nicolas M D A van Mieghem, Ricardo P J Budde
{"title":"Erratum for: Photon-counting Detector CT in Patients Pre- and Post-Transcatheter Aortic Valve Replacement.","authors":"Judith van der Bie, Simran P Sharma, Marcel van Straten, Daniel Bos, Alexander Hirsch, Marcel L Dijkshoorn, Rik Adrichem, Nicolas M D A van Mieghem, Ricardo P J Budde","doi":"10.1148/ryct.239002","DOIUrl":"10.1148/ryct.239002","url":null,"abstract":"","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"5 6","pages":"e239002"},"PeriodicalIF":7.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080886","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}
Aishwarya Gulati, Vaibhav Gulati, Ray Hu, Prabhakar Shantha Rajiah, Jadranka Stojanovska, Jennifer Febbo, Harold I Litt, Behzad Pavri, Baskaran Sundaram
James Dundas, Jonathon A Leipsic, Stephanie Sellers, Philipp Blanke, Patricia Miranda, Nicholas Ng, Sarah Mullen, David Meier, Mariama Akodad, Janarthanan Sathananthan, Carlos Collet, Bernard de Bruyne, Olivier Muller, Georgios Tzimas
{"title":"Artificial Intelligence-based Coronary Stenosis Quantification at Coronary CT Angiography versus Quantitative Coronary Angiography.","authors":"James Dundas, Jonathon A Leipsic, Stephanie Sellers, Philipp Blanke, Patricia Miranda, Nicholas Ng, Sarah Mullen, David Meier, Mariama Akodad, Janarthanan Sathananthan, Carlos Collet, Bernard de Bruyne, Olivier Muller, Georgios Tzimas","doi":"10.1148/ryct.230124","DOIUrl":"10.1148/ryct.230124","url":null,"abstract":"<p><p>Purpose To evaluate the performance of a new artificial intelligence (AI)-based tool by comparing the quantified stenosis severity at coronary CT angiography (CCTA) with a reference standard derived from invasive quantitative coronary angiography (QCA). Materials and Methods This secondary, post hoc analysis included 120 participants (mean age, 59.7 years ± 10.8 [SD]; 73 [60.8%] men, 47 [39.2%] women) from three large clinical trials (AFFECTS, P3, REFINE) who underwent CCTA and invasive coronary angiography with QCA. Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95; <i>P</i> < .001) and 0.93 (95% CI: 0.89, 0.97; <i>P</i> < .001), respectively. The AUCs to predict DS of 50% or greater and 70% or greater on a per-patient basis were 0.93 (95% CI: 0.88, 0.97; <i>P</i> < .001) and 0.88 (95% CI: 0.81, 0.94; <i>P</i> < .001), respectively. Conclusion AI-CSQ at CCTA demonstrated a high diagnostic performance compared with QCA both on a per-patient and per-vessel basis, with high sensitivity for stenosis detection. <b>Keywords:</b> CT Angiography, Cardiac, Coronary Arteries <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"5 6","pages":"e230124"},"PeriodicalIF":7.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080882","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}
João Matos, Emmi Helle, Melanie Care, Yasbanoo Moayedi, Michael H Gollob, Paaladinesh Thavendiranathan, Danna Spears, Kate Hanneman
{"title":"Cardiac MRI and Clinical Outcomes in <i>TMEM43</i> Arrhythmogenic Cardiomyopathy.","authors":"João Matos, Emmi Helle, Melanie Care, Yasbanoo Moayedi, Michael H Gollob, Paaladinesh Thavendiranathan, Danna Spears, Kate Hanneman","doi":"10.1148/ryct.230155","DOIUrl":"10.1148/ryct.230155","url":null,"abstract":"<p><p>Arrhythmogenic cardiomyopathy is an inherited cardiomyopathy that can involve both ventricles. Several genes have been identified as pathogenic in arrhythmogenic cardiomyopathy, including <i>TMEM43</i>. However, there are limited data on cardiac MRI findings in patients with <i>TMEM43</i> variants to date. In this case series, cardiac MRI findings and clinical outcomes are described in 14 patients with <i>TMEM43</i> variants, including eight (57%) with the pathogenic p.Ser358Leu variant (six female patients; mean age, 33 years ± 15 [SD]) and six (43%) with a <i>TMEM43</i> variant of unknown significance (three female patients; mean age, 38 years ± 11). MRI findings demonstrated left ventricular systolic dysfunction in eight (57%) patients and right ventricular dysfunction in four (29%) patients. Among the nine patients with late gadolinium enhancement imaging, left ventricular late gadolinium enhancement was present in seven (78%; all subepicardial) patients. In summary, <i>TMEM43</i> variants are associated with high prevalence of subepicardial late gadolinium enhancement and left ventricular dysfunction. <b>Keywords:</b> Arrhythmogenic Cardiomyopathy, Arrhythmogenic Right Ventricular Cardiomyopathy, <i>TMEM43</i>, Cardiac MRI, Genetic Variants <i>Supplemental material is available for this article</i>.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"5 6","pages":"e230155"},"PeriodicalIF":7.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080883","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}