TY - JOUR
T1 - Preoperative assessment of parietal pleural invasion/adhesion of subpleural lung cancer
T2 - advantage of software-assisted analysis of 4-dimensional dynamic-ventilation computed tomography
AU - for the ACTIve Study Group investigators
AU - Yamashiro, Tsuneo
AU - Moriya, Hiroshi
AU - Tsubakimoto, Maho
AU - Nagatani, Yukihiro
AU - Kimoto, Tatsuya
AU - Murayama, Sadayuki
AU - Sakuma, Kotaro
AU - Sakai, Fumikazu
AU - Iwasawa, Tae
AU - Nitta, Norihisa
AU - Murata, Kiyoshi
AU - Yanagawa, Masahiro
AU - Honda, Osamu
AU - Tomiyama, Noriyuki
AU - Koyama, Mitsuhiro
AU - Nishimoto, Yuko
AU - Noma, Satoshi
AU - Ohno, Yoshiharu
AU - Aoki, Takatoshi
AU - Yamashiro, Tsuneo
AU - Xu, Yanyan
N1 - Publisher Copyright:
© 2019, European Society of Radiology.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Objective: To evaluate the accuracy of four-dimensional (4D) dynamic-ventilation computed tomography (CT) scanning coupled with our novel image analysis software to diagnose parietal pleural invasion/adhesion of peripheral (subpleural) lung cancer. Methods: Eighteen patients with subpleural lung cancer underwent both 4D dynamic-ventilation CT during free breathing and conventional (static) chest CT during preoperative assessment. The absence of parietal pleural invasion/adhesion was surgically confirmed in 13 patients, while the presence of parietal pleural invasion/adhesion was confirmed in 5 patients. Two chest radiologists, who were blinded to patient status, cooperatively evaluated the presence of pleural invasion/adhesion using two different imaging modalities: (i) conventional high-resolution CT images, reconstructed in the axial, coronal, and sagittal directions, and (ii) 4D dynamic-ventilation CT images combined with a color map created by image analysis software to visualize movement differences between the lung surface and chest wall. Parameters of diagnostic accuracy were assessed, including a receiver operating characteristic analysis. Results: Software-assisted 4D dynamic-ventilation CT images achieved perfect diagnostic accuracy for pleural invasion/adhesion (sensitivity, 100%; specificity, 100%; area under the curve [AUC], 1.000) compared to conventional chest CT (sensitivity, 60%; specificity, 77%; AUC, 0.846). Conclusion: Software-assisted 4D dynamic-ventilation CT can be considered as a novel imaging approach for accurate preoperative analysis of pleural invasion/adhesion of peripheral lung cancer. Key Points: • 4D dynamic-ventilation CT can correctly assess parietal pleural invasion/adhesion of peripheral lung cancer. • A unique color map clearly demonstrates parietal pleural invasion/adhesion. • Our technique can be expanded to diagnose “benign” pleural adhesions for safer thoracoscopic surgery.
AB - Objective: To evaluate the accuracy of four-dimensional (4D) dynamic-ventilation computed tomography (CT) scanning coupled with our novel image analysis software to diagnose parietal pleural invasion/adhesion of peripheral (subpleural) lung cancer. Methods: Eighteen patients with subpleural lung cancer underwent both 4D dynamic-ventilation CT during free breathing and conventional (static) chest CT during preoperative assessment. The absence of parietal pleural invasion/adhesion was surgically confirmed in 13 patients, while the presence of parietal pleural invasion/adhesion was confirmed in 5 patients. Two chest radiologists, who were blinded to patient status, cooperatively evaluated the presence of pleural invasion/adhesion using two different imaging modalities: (i) conventional high-resolution CT images, reconstructed in the axial, coronal, and sagittal directions, and (ii) 4D dynamic-ventilation CT images combined with a color map created by image analysis software to visualize movement differences between the lung surface and chest wall. Parameters of diagnostic accuracy were assessed, including a receiver operating characteristic analysis. Results: Software-assisted 4D dynamic-ventilation CT images achieved perfect diagnostic accuracy for pleural invasion/adhesion (sensitivity, 100%; specificity, 100%; area under the curve [AUC], 1.000) compared to conventional chest CT (sensitivity, 60%; specificity, 77%; AUC, 0.846). Conclusion: Software-assisted 4D dynamic-ventilation CT can be considered as a novel imaging approach for accurate preoperative analysis of pleural invasion/adhesion of peripheral lung cancer. Key Points: • 4D dynamic-ventilation CT can correctly assess parietal pleural invasion/adhesion of peripheral lung cancer. • A unique color map clearly demonstrates parietal pleural invasion/adhesion. • Our technique can be expanded to diagnose “benign” pleural adhesions for safer thoracoscopic surgery.
KW - Computer-assisted image analysis
KW - Four-dimensional computed tomography
KW - Lung cancer
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U2 - 10.1007/s00330-019-06131-w
DO - 10.1007/s00330-019-06131-w
M3 - Article
C2 - 30915563
AN - SCOPUS:85064070885
SN - 0938-7994
VL - 29
SP - 5247
EP - 5252
JO - European Radiology
JF - European Radiology
IS - 10
ER -