Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.
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| Title: | Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease. |
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| Authors: | Lin, Yue1 (AUTHOR), Li, Miao2 (AUTHOR), Xie, Sheng1 (AUTHOR) Xs2025@126.com |
| Source: | European Radiology. Nov2025, Vol. 35 Issue 11, p6983-6995. 13p. |
| Subjects: | Pulmonary hypertension, Artificial intelligence, Diagnostic imaging, Clinical decision making, Computer-assisted image analysis (Medicine), Thromboembolism |
| Abstract: | Objectives: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED). Methods: A single-center, retrospective analysis of 350 sequential patients with right heart catheterization (RHC)-confirmed CTEPH, CTED, and normal controls was conducted. Parameters such as the main pulmonary artery diameter (MPAd), the ratio of MPA to ascending aorta diameter (MPAd/AAd), the ratio of right to left ventricle diameter (RVd/LVd), and the ratio of RV to LV volume (RVv/LVv) were evaluated using automated AI software and compared with manual analysis. The reliability was assessed through an intraclass correlation coefficient (ICC) analysis. The diagnostic accuracy was determined using receiver-operating characteristic (ROC) curves. Results: Compared to CTED and control groups, CTEPH patients were significantly more likely to have elevated automatic CTPA metrics (all p < 0.001, respectively). Automated MPAd, MPAd/Aad, and RVv/LVv had a strong correlation with mPAP (r = 0.952, 0.904, and 0.815, respectively, all p < 0.001). The automated and manual CTPA analyses showed strong concordance. For the CTEPH and CTED categories, the optimal area under the curve (AU-ROC) reached 0.939 (CI: 0.908–0.969). In the CTEPH and control groups, the best AU-ROC was 0.970 (CI: 0.953–0.988). In the CTED and control groups, the best AU-ROC was 0.782 (CI: 0.724–0.840). Conclusion: Automated AI-driven CTPA analysis provides a dependable approach for evaluating patients with CTEPH, CTED, and normal controls, demonstrating excellent consistency and efficiency. Key Points: QuestionGuidelines do not advocate for applying treatment protocols for CTEPH to patients with CTED; early detection of the condition is crucial. FindingsAutomated CTPA analysis was feasible in 100% of patients with good agreement and would have added information for early detection and identification. Clinical relevanceAutomated AI-driven CTPA analysis provides a reliable approach demonstrating excellent consistency and efficiency. Additionally, these noninvasive imaging findings may aid in treatment stratification and determining optimal intervention directed by RHC. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 188901914 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Yue%22">Lin, Yue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Miao%22">Li, Miao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Sheng%22">Xie, Sheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Xs2025@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Nov2025, Vol. 35 Issue 11, p6983-6995. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Pulmonary+hypertension%22">Pulmonary hypertension</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+decision+making%22">Clinical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Thromboembolism%22">Thromboembolism</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED). Methods: A single-center, retrospective analysis of 350 sequential patients with right heart catheterization (RHC)-confirmed CTEPH, CTED, and normal controls was conducted. Parameters such as the main pulmonary artery diameter (MPAd), the ratio of MPA to ascending aorta diameter (MPAd/AAd), the ratio of right to left ventricle diameter (RVd/LVd), and the ratio of RV to LV volume (RVv/LVv) were evaluated using automated AI software and compared with manual analysis. The reliability was assessed through an intraclass correlation coefficient (ICC) analysis. The diagnostic accuracy was determined using receiver-operating characteristic (ROC) curves. Results: Compared to CTED and control groups, CTEPH patients were significantly more likely to have elevated automatic CTPA metrics (all p < 0.001, respectively). Automated MPAd, MPAd/Aad, and RVv/LVv had a strong correlation with mPAP (r = 0.952, 0.904, and 0.815, respectively, all p < 0.001). The automated and manual CTPA analyses showed strong concordance. For the CTEPH and CTED categories, the optimal area under the curve (AU-ROC) reached 0.939 (CI: 0.908–0.969). In the CTEPH and control groups, the best AU-ROC was 0.970 (CI: 0.953–0.988). In the CTED and control groups, the best AU-ROC was 0.782 (CI: 0.724–0.840). Conclusion: Automated AI-driven CTPA analysis provides a dependable approach for evaluating patients with CTEPH, CTED, and normal controls, demonstrating excellent consistency and efficiency. Key Points: QuestionGuidelines do not advocate for applying treatment protocols for CTEPH to patients with CTED; early detection of the condition is crucial. FindingsAutomated CTPA analysis was feasible in 100% of patients with good agreement and would have added information for early detection and identification. Clinical relevanceAutomated AI-driven CTPA analysis provides a reliable approach demonstrating excellent consistency and efficiency. Additionally, these noninvasive imaging findings may aid in treatment stratification and determining optimal intervention directed by RHC. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-025-11698-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 6983 Subjects: – SubjectFull: Pulmonary hypertension Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Clinical decision making Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Thromboembolism Type: general Titles: – TitleFull: Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Yue – PersonEntity: Name: NameFull: Li, Miao – PersonEntity: Name: NameFull: Xie, Sheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 35 – Type: issue Value: 11 Titles: – TitleFull: European Radiology Type: main |
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