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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F25%3A10498418" target="_blank" >RIV/00064165:_____/25:10498418 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/25:10498418 RIV/00216208:11130/25:10498418 RIV/00216305:26220/26:0198275 RIV/00064203:_____/25:10498418

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6n-XbHvtq-" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6n-XbHvtq-</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00330-025-11678-y" target="_blank" >10.1007/s00330-025-11678-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 &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: Question This study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. Findings Quantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevance The automated quantification of perfusion changes aligns closely with radiologists&apos; evaluations, delivering a standardized, reproducible measure with clinical relevance.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 &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: Question This study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. Findings Quantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevance The automated quantification of perfusion changes aligns closely with radiologists&apos; evaluations, delivering a standardized, reproducible measure with clinical relevance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Radiology

ISSN

0938-7994

e-ISSN

1432-1084

Svazek periodika

35

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

6996-7003

Kód UT WoS článku

001498335900001

EID výsledku v databázi Scopus

2-s2.0-105007079831