Preoperative assessment of localized pleural adhesion: Utility of software-assisted analysis on dynamic-ventilation computed tomography
•Movie images on dynamic-ventilation computed tomography reduced the false negative ratio in localized pleural adhesion (LPA) detection.•LPA detectability was improved by combing movie images with a three- dimensional color map based on dedicated software analysis.•Information provided to thoracic s...
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Published in | European journal of radiology Vol. 133; p. 109347 |
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Abstract | •Movie images on dynamic-ventilation computed tomography reduced the false negative ratio in localized pleural adhesion (LPA) detection.•LPA detectability was improved by combing movie images with a three- dimensional color map based on dedicated software analysis.•Information provided to thoracic surgeons could be improved only by adding dynamic-ventilation CT to pre-operative routine chest CT series.
To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).
Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.
Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.
Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA. |
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AbstractList | To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).
Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.
Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.
Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA. To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).PURPOSETo assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.MATERIALS AND METHODSFifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.RESULTSMild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA.CONCLUSIONSoftware-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA. •Movie images on dynamic-ventilation computed tomography reduced the false negative ratio in localized pleural adhesion (LPA) detection.•LPA detectability was improved by combing movie images with a three- dimensional color map based on dedicated software analysis.•Information provided to thoracic surgeons could be improved only by adding dynamic-ventilation CT to pre-operative routine chest CT series. To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA). Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects. Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %. Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA. |
ArticleNumber | 109347 |
Author | Oshio, Yasuhiko Sato, Shigetaka Nagatani, Yukihiro Tsukagoshi, Shinsuke Usio, Noritoshi Hanaoka, Jun Yamashiro, Tsuneo Hashimoto, Masayuki Yoshigoe, Makoto Uemura, Ryo Nitta, Norihisa Murata, Kiyoshi Fukunaga, Kentaro Moriya, Hiroshi Watanabe, Yoshiyuki Kimoto, Tatsuya |
Author_xml | – sequence: 1 givenname: Yukihiro surname: Nagatani fullname: Nagatani, Yukihiro email: yatsushi@belle.shiga-med.ac.jp organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 2 givenname: Masayuki surname: Hashimoto fullname: Hashimoto, Masayuki organization: Department of Thoracic Surgery, Kyoto Medical Center, Kyoto, Kyoto, 612-8555, Japan – sequence: 3 givenname: Yasuhiko surname: Oshio fullname: Oshio, Yasuhiko organization: Division of General Thoracic Surgery, Department of Surgery, Shiga University of Medical Science, Seta-tsukinowa-cho, Otsu, Shiga, 520-2192, Japan – sequence: 4 givenname: Shigetaka surname: Sato fullname: Sato, Shigetaka organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 5 givenname: Jun surname: Hanaoka fullname: Hanaoka, Jun organization: Division of General Thoracic Surgery, Department of Surgery, Shiga University of Medical Science, Seta-tsukinowa-cho, Otsu, Shiga, 520-2192, Japan – sequence: 6 givenname: Kentaro surname: Fukunaga fullname: Fukunaga, Kentaro organization: Division of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Seta-tsukinowa-cho, Otsu, Shiga, 520-2192, Japan – sequence: 7 givenname: Ryo surname: Uemura fullname: Uemura, Ryo organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 8 givenname: Makoto surname: Yoshigoe fullname: Yoshigoe, Makoto organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 9 givenname: Norihisa surname: Nitta fullname: Nitta, Norihisa organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 10 givenname: Noritoshi surname: Usio fullname: Usio, Noritoshi organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 11 givenname: Shinsuke surname: Tsukagoshi fullname: Tsukagoshi, Shinsuke organization: CT System Division, Canon Medical Systems, Otawara, Tochigi, 324-8550, Japan – sequence: 12 givenname: Tatsuya surname: Kimoto fullname: Kimoto, Tatsuya organization: Department of Radio Center for Medical Research and Development, Canon Medical Systems, Otawara, Tochigi, 324-8550, Japan – sequence: 13 givenname: Tsuneo surname: Yamashiro fullname: Yamashiro, Tsuneo organization: Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Nishihara, Okinawa, 903-0215, Japan – sequence: 14 givenname: Hiroshi surname: Moriya fullname: Moriya, Hiroshi organization: Department of Radiology, Ohara General Hospital, Fukushima, Fukushima, 960-8611, Japan – sequence: 15 givenname: Kiyoshi surname: Murata fullname: Murata, Kiyoshi organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan – sequence: 16 givenname: Yoshiyuki surname: Watanabe fullname: Watanabe, Yoshiyuki organization: Department of Radiology, Shiga University of Medical Science, Otsu, Shiga, 520-2192, Japan |
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Keywords | Ultra-low-dose scanning Software-assisted adhesion detection Thoracic surgery Four-dimensional computed tomography Iterative reconstruction Pleural adhesion |
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Snippet | •Movie images on dynamic-ventilation computed tomography reduced the false negative ratio in localized pleural adhesion (LPA) detection.•LPA detectability was... To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA). Fifty-one patients scheduled to undergo... To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).PURPOSETo assess the usefulness of software... |
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SubjectTerms | Four-dimensional computed tomography Humans Iterative reconstruction Lung Pleural adhesion Pleural Diseases - diagnostic imaging Radiographic Image Interpretation, Computer-Assisted Respiration Software Software-assisted adhesion detection Thoracic surgery Tomography, X-Ray Computed Ultra-low-dose scanning |
Title | Preoperative assessment of localized pleural adhesion: Utility of software-assisted analysis on dynamic-ventilation computed tomography |
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