Use of artificial intelligence and radiomics for risk stratification in patients with pulmonary embolism: New tools for an old problem

European Journal of Clinical InvestigationEarly View e14171 EDITORIAL Use of artificial intelligence and radiomics for risk stratification in patients with pulmonary embolism: New tools for an old problem Davide Santagata, Davide Santagata orcid.org/0000-0003-1748-960X Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, ItalySearch for more papers by this authorMarco Paolo Donadini, Marco Paolo Donadini orcid.org/0000-0001-5065-318X Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, ItalySearch for more papers by this authorWalter Ageno, Corresponding Author Walter Ageno [email protected] orcid.org/0000-0002-1922-8879 Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, Italy Correspondence Walter Ageno, Department of Medicine and Surgery, Research Center on Thromboembolic Disorders and Antithrombotic Therapies, University of Insubria, Via Gucciardini 9, Varese, Italy. Email: [email protected]Search for more papers by this author Davide Santagata, Davide Santagata orcid.org/0000-0003-1748-960X Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, ItalySearch for more papers by this authorMarco Paolo Donadini, Marco Paolo Donadini orcid.org/0000-0001-5065-318X Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, ItalySearch for more papers by this authorWalter Ageno, Corresponding Author Walter Ageno [email protected] orcid.org/0000-0002-1922-8879 Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, Italy Correspondence Walter Ageno, Department of Medicine and Surgery, Research Center on Thromboembolic Disorders and Antithrombotic Therapies, University of Insubria, Via Gucciardini 9, Varese, Italy. Email: [email protected]Search for more papers by this author First published: 24 January 2024 https://doi.org/10.1111/eci.14171Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat CONFLICT OF INTEREST STATEMENT Walter Ageno: research support from Bayer and advisory boards for Astra Zeneca, Bayer, BMS-Pfizer, Norgine, Sanofi, Techdow, Viatris. Davide Santagata: nothing to disclose. Marco Paolo Donadini: nothing to disclose. REFERENCES 1Goldhaber SZ, Bounameaux H. Pulmonary embolism and deep vein thrombosis. Lancet. 2012; 379(9828): 1835-1846. 10.1016/S0140-6736(11)61904-1 PubMedWeb of Science®Google Scholar 2Konstantinides SV, Meyer G, Becattini C, et al. 2019 ESC guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS). Eur Heart J. 2020; 41(4): 543-603. 10.1093/eurheartj/ehz405 PubMedWeb of Science®Google Scholar 3Doupe P, Faghmous J, Basu S. Machine learning for health services researchers. Value Health. 2019; 22(7): 808-815. 10.1016/j.jval.2019.02.012 PubMedWeb of Science®Google Scholar 4Huang Y, Li J, Li M, Aparasu RR. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Med Res Methodol. 2023; 23(1): 268. 10.1186/s12874-023-02078-1 PubMedWeb of Science®Google Scholar 5Zou S, Wu Z. A narrative review of the application of machine learning in venous thromboembolism. Vascular. 2023. doi:10.1177/17085381231153216 10.1177/17085381231153216 Web of Science®Google Scholar 6Franco-Moreno A, Muñoz-Rivas N, Ruiz-Giardín JM, de Ancos-Aracil C. Artificial intelligence for recurrence in patients with venous thromboembolism: towards a new era. Rev Clin Esp (Barc). 2023; 223(7): 456-459. 10.1016/j.rce.2023.04.006 CASPubMedWeb of Science®Google Scholar 7Jha AK, Mithun S, Sherkhane UB, et al. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. Explor Target Antitumor Ther. 2023; 4(4): 569-582. 10.37349/etat.2023.00153 PubMedGoogle Scholar 8Topff L, Ranschaert ER, Bartels-Rutten A, et al. Artificial intelligence tool for detection and worklist prioritization reduces time to diagnosis of incidental pulmonary embolism at CT. Radiol Cardiothorac Imaging. 2023; 5(2):e220163. 10.1148/ryct.220163 PubMedGoogle Scholar 9Baeza S, Gil D, Garcia-Olivé I, et al. Correction: a novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients. EJNMMI Phys. 2023; 10(1): 13. 10.1186/s40658-023-00532-z PubMedWeb of Science®Google Scholar 10Cheikh AB, Gorincour G, Nivet H, et al. How artificial intelligence improves radiological interpretation in suspected pulmonary embolism. Eur Radiol. 2022; 32(9): 5831-5842. 10.1007/s00330-022-08645-2 CASPubMedWeb of Science®Google Scholar 11Ma X, Ferguson EC, Jiang X, Savitz SI, Shams S. A multitask deep learning approach for pulmonary embolism detection and identification. Sci Rep. 2022; 12(1): 13087. 10.1038/s41598-022-16976-9 CASPubMedWeb of Science®Google Scholar 12Su H, Han Z, Fu Y, et al. Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques. Front Neuroinform. 2022; 16:1029690. 10.3389/fninf.2022.1029690 PubMedWeb of Science®Google Scholar 13Gao Y, Wang Y, Cao X, et al. Rapid prediction of deterioration risk among non-high-risk patients with acute pulmonary embolism at admission: an imaging tool. Int J Cardiol. 2021; 338: 229-236. 10.1016/j.ijcard.2021.06.013 PubMedWeb of Science®Google Scholar 14Gotta J, Koch V, Geyer T, et al. Imaging-based risk stratification of patients with pulmonary embolism based on dual-energy CT-derived radiomics. Eur J Clin Investig. 2023;e14139. 10.1111/eci.14139 PubMedWeb of Science®Google Scholar 15Weintraub SF, You J, Wilson S, Galmer A. The challenge of intermediate-risk pulmonary embolism. Am J Ther. 2023; 30(2): e134-e144. 10.1097/MJT.0000000000001605 PubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issuee14171 ReferencesRelatedInformation