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Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH.

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Title: Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH.
Authors: Suchanek, Vojtech1 (AUTHOR), Jakubicek, Roman2 (AUTHOR), Hrdlicka, Jan3 (AUTHOR), Novak, Matej3 (AUTHOR), Miksova, Lucie4 (AUTHOR), Jansa, Pavel4 (AUTHOR), Burgetova, Andrea3 (AUTHOR), Lambert, Lukas1 (AUTHOR) lambert.lukas@gmail.com
Source: European Radiology. Nov2025, Vol. 35 Issue 11, p6996-7003. 8p.
Subjects: Bayesian analysis, Pulmonary arterial hypertension, Statistical measurement, Machine learning, Computer-assisted image analysis (Medicine), Biomarkers, Pulmonary circulation, Image segmentation
Abstract: Objectives: This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Materials and methods: Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. Results: CTPA scans from 52 patients (mean age, 65.2 ± 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, ρ = −0.485, p = 0.001), mean pulmonary arterial pressure (ρ = −0.417, p = 0.002) and pulmonary vascular resistance (ρ = −0.556, p < 0.0001), mosaic attenuation (ρ = −0.527, p < 0.0001), perfusion centralization (ρ = −0.489, p = < 0.0001), and right ventricular diameter (ρ = −0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (ρ = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (ρ = −0.477, p < 0.0001), and with proBNP (ρ = −0.463, p = 0.002). Conclusion: This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures. Key Points: QuestionThis study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. FindingsQuantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevanceThe automated quantification of perfusion changes aligns closely with radiologists' evaluations, delivering a standardized, reproducible measure with clinical relevance. [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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  Data: Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Suchanek%2C+Vojtech%22&quot;&gt;Suchanek, Vojtech&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Jakubicek%2C+Roman%22&quot;&gt;Jakubicek, Roman&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Hrdlicka%2C+Jan%22&quot;&gt;Hrdlicka, Jan&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Novak%2C+Matej%22&quot;&gt;Novak, Matej&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Miksova%2C+Lucie%22&quot;&gt;Miksova, Lucie&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Jansa%2C+Pavel%22&quot;&gt;Jansa, Pavel&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Burgetova%2C+Andrea%22&quot;&gt;Burgetova, Andrea&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Lambert%2C+Lukas%22&quot;&gt;Lambert, Lukas&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; lambert.lukas@gmail.com&lt;/i&gt;
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Nov2025, Vol. 35 Issue 11, p6996-7003. 8p.
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  Data: Objectives: This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Materials and methods: Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. Results: CTPA scans from 52 patients (mean age, 65.2 &#177; 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, ρ = −0.485, p = 0.001), mean pulmonary arterial pressure (ρ = −0.417, p = 0.002) and pulmonary vascular resistance (ρ = −0.556, p &lt; 0.0001), mosaic attenuation (ρ = −0.527, p &lt; 0.0001), perfusion centralization (ρ = −0.489, p = &lt; 0.0001), and right ventricular diameter (ρ = −0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (ρ = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (ρ = −0.477, p &lt; 0.0001), and with proBNP (ρ = −0.463, p = 0.002). Conclusion: This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures. Key Points: QuestionThis study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. FindingsQuantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevanceThe automated quantification of perfusion changes aligns closely with radiologists&#39; evaluations, delivering a standardized, reproducible measure with clinical relevance. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;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&#39;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.&lt;/i&gt; (Copyright applies to all Abstracts.)
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