Literature DB >> 26663211

Prediction of anatomical lung volume using planimetric measurements on chest radiographs.

Chul Hwan Park1, Seok Jin Haam2, Sungsoo Lee2, Kyung Hwa Han3, Tae Hoon Kim4.   

Abstract

BACKGROUND: The anatomical lung volume is conventionally measured by computed tomography (CT). However, chest radiographs could be considered as an alternative method with low cost and low radiation.
PURPOSE: To predict the anatomical lung volume using planimetric measurements of chest radiographs.
MATERIAL AND METHODS: In total, 119 participants (M:F ratio = 66:53; age, 53.7 ± 9.6 years) who underwent chest CT for lung cancer screening were enrolled. The lung volume on CT was measured as a reference for the anatomical lung volume. To eliminate the bias from the degree of inspiration, virtual chest radiographs (posterior-anterior view and lateral view) were generated from the CT images using the thick multiplanar technique, and the lung area (cm(2)) was measured in the right (P), left (Q), and lateral (R) lungs according to the planimetric method. A regression equation predicting the anatomical lung volume from the planimetric measurements was generated. The correlation between the measured and estimated lung volumes was evaluated. The percentage error rate (%) was calculated and the equation was validated internally and externally.
RESULTS: The equation predicting the anatomical lung volume (mL) was 9.6*S-1367, where the summed lung area (S) was defined as (P + Q + R). The measured and estimated lung volumes were highly correlated (R = 0.941, P < 0.001). The absolute error rate was 5.7 ± 4.9%. The root mean square error of the equation was 290.2. The root mean square errors on internal and external validation were 300.4 and 267.0.
CONCLUSION: The anatomical lung volume may be feasibly and accurately predicted from planimetric measurements of chest radiographs. © The Foundation Acta Radiologica 2015.

Entities:  

Keywords:  Computed tomography (CT); chest radiograph; lung volume; planimetric measurement

Mesh:

Year:  2015        PMID: 26663211     DOI: 10.1177/0284185115618548

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  3 in total

1.  The repeatability of computed tomography lung volume measurements: Comparisons in healthy subjects, patients with obstructive lung disease, and patients with restrictive lung disease.

Authors:  Jae Min Shin; Tae Hoon Kim; Seokjin Haam; Kyunghwa Han; Min Kwang Byun; Yoon Soo Chang; Hyung Jung Kim; Chul Hwan Park
Journal:  PLoS One       Date:  2017-08-10       Impact factor: 3.240

2.  Automated estimation of total lung volume using chest radiographs and deep learning.

Authors:  Ecem Sogancioglu; Keelin Murphy; Ernst Th Scholten; Luuk H Boulogne; Mathias Prokop; Bram van Ginneken
Journal:  Med Phys       Date:  2022-04-18       Impact factor: 4.506

3.  Projected lung areas using dynamic X-ray (DXR).

Authors:  Takuya Hino; Akinori Hata; Tomoyuki Hida; Yoshitake Yamada; Masako Ueyama; Tetsuro Araki; Takeshi Kamitani; Mizuki Nishino; Atsuko Kurosaki; Masahiro Jinzaki; Kousei Ishigami; Hiroshi Honda; Hiroto Hatabu; Shoji Kudoh
Journal:  Eur J Radiol Open       Date:  2020-09-12
  3 in total

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