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
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; P < .001) and 0.93 (95% CI: 0.89, 0.97; P < .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; P < .001) and 0.88 (95% CI: 0.81, 0.94; P < .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. Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. Published under a CC BY 4.0 license.
{"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
Arrhythmogenic cardiomyopathy is an inherited cardiomyopathy that can involve both ventricles. Several genes have been identified as pathogenic in arrhythmogenic cardiomyopathy, including TMEM43. However, there are limited data on cardiac MRI findings in patients with TMEM43 variants to date. In this case series, cardiac MRI findings and clinical outcomes are described in 14 patients with TMEM43 variants, including eight (57%) with the pathogenic p.Ser358Leu variant (six female patients; mean age, 33 years ± 15 [SD]) and six (43%) with a TMEM43 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, TMEM43 variants are associated with high prevalence of subepicardial late gadolinium enhancement and left ventricular dysfunction. Keywords: Arrhythmogenic Cardiomyopathy, Arrhythmogenic Right Ventricular Cardiomyopathy, TMEM43, Cardiac MRI, Genetic Variants Supplemental material is available for this article.
{"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}