Anantharaman Ramasamy, Hessam Sokooti, Xiaotong Zhang, Evangelia Tzorovili, Retesh Bajaj, Pieter Kitslaar, Alexander Broersen, Rajiv Amersey, Ajay Jain, Mick Ozkor, Johan H C Reiber, Jouke Dijkstra, Patrick W Serruys, James C Moon, Anthony Mathur, Andreas Baumbach, Ryo Torii, Francesca Pugliese, Christos V Bourantas
{"title":"新的基于近红外光谱血管内超声的深度学习方法用于精确的冠状动脉计算机断层扫描斑块定量和表征。","authors":"Anantharaman Ramasamy, Hessam Sokooti, Xiaotong Zhang, Evangelia Tzorovili, Retesh Bajaj, Pieter Kitslaar, Alexander Broersen, Rajiv Amersey, Ajay Jain, Mick Ozkor, Johan H C Reiber, Jouke Dijkstra, Patrick W Serruys, James C Moon, Anthony Mathur, Andreas Baumbach, Ryo Torii, Francesca Pugliese, Christos V Bourantas","doi":"10.1093/ehjopen/oead090","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).</p><p><strong>Methods and results: </strong>Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (Δ<sub>DL-NIRS-IVUS</sub>: -37.8 ± 89.0 vs. Δ<sub>Conv-NIRS-IVUS</sub>: 243.3 ± 183.7 mm3, variance ratio: 4.262, <i>P</i> < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, <i>P</i> < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, <i>P</i> < 0.001) and quantified minimum lumen area (Δ<sub>DL-NIRS-IVUS</sub>: -0.35 ± 1.81 vs. Δ<sub>Conv-NIRS-IVUS</sub>: 1.37 ± 2.32 mm<sup>2</sup>, variance ratio: 1.634, <i>P</i> < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, <i>P</i> = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, <i>P</i> < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s.</p><p><strong>Conclusions: </strong>The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).</p>","PeriodicalId":93995,"journal":{"name":"European heart journal open","volume":"3 5","pages":"oead090"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615127/pdf/","citationCount":"0","resultStr":"{\"title\":\"Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.\",\"authors\":\"Anantharaman Ramasamy, Hessam Sokooti, Xiaotong Zhang, Evangelia Tzorovili, Retesh Bajaj, Pieter Kitslaar, Alexander Broersen, Rajiv Amersey, Ajay Jain, Mick Ozkor, Johan H C Reiber, Jouke Dijkstra, Patrick W Serruys, James C Moon, Anthony Mathur, Andreas Baumbach, Ryo Torii, Francesca Pugliese, Christos V Bourantas\",\"doi\":\"10.1093/ehjopen/oead090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).</p><p><strong>Methods and results: </strong>Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (Δ<sub>DL-NIRS-IVUS</sub>: -37.8 ± 89.0 vs. Δ<sub>Conv-NIRS-IVUS</sub>: 243.3 ± 183.7 mm3, variance ratio: 4.262, <i>P</i> < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, <i>P</i> < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, <i>P</i> < 0.001) and quantified minimum lumen area (Δ<sub>DL-NIRS-IVUS</sub>: -0.35 ± 1.81 vs. Δ<sub>Conv-NIRS-IVUS</sub>: 1.37 ± 2.32 mm<sup>2</sup>, variance ratio: 1.634, <i>P</i> < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, <i>P</i> = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, <i>P</i> < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s.</p><p><strong>Conclusions: </strong>The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).</p>\",\"PeriodicalId\":93995,\"journal\":{\"name\":\"European heart journal open\",\"volume\":\"3 5\",\"pages\":\"oead090\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615127/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European heart journal open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/ehjopen/oead090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European heart journal open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ehjopen/oead090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.
Aims: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).
Methods and results: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s.
Conclusions: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).
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