Publication: Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography
Issued Date
2021-04-01
Resource Type
ISSN
22234306
22234292
22234292
Other identifier(s)
2-s2.0-85101301611
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Mahidol University
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SCOPUS
Bibliographic Citation
Quantitative Imaging in Medicine and Surgery. Vol.11, No.4 (2021), 1134-1143
Suggested Citation
Ramandeep Singh, Mannudeep K. Kalra, Fatemeh Homayounieh, Chayanin Nitiwarangkul, Shaunagh McDermott, Brent P. Little, Inga T. Lennes, Jo Anne O. Shepard, Subba R. Digumarthy Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quantitative Imaging in Medicine and Surgery. Vol.11, No.4 (2021), 1134-1143. doi:10.21037/qims-20-630 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/78319
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Title
Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography
Abstract
Background: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) helps early lung cancer detection, commonly presenting as small pulmonary nodules. Artificial intelligence (AI)-based vessel suppression (AI-VS) and automatic detection (AI-AD) algorithm can improve detection of subsolid nodules (SSNs) on LDCT. We assessed the impact of AI-VS and AI-AD in detection and classification of SSNs [ground-glass nodules (GGNs) and part-solid nodules (PSNs)], on LDCT performed for LCS. Methods: Following regulatory approval, 123 LDCT examinations with sub-solid pulmonary nodules (average diameter ≥6 mm) were processed to generate three image series for each examination-unprocessed, AI-VS, and AI-AD series with annotated lung nodules. Two thoracic radiologists in consensus formed the standard of reference (SOR) for this study. Two other thoracic radiologists (R1 and R2; 5 and 10 years of experience in thoracic CT image interpretation) independently assessed the unprocessed images alone, then together with AI-VS series, and finally with AI-AD for detecting all ≥6 mm GGN and PSN. We performed receiver operator characteristics (ROC) and Cohen’s Kappa analyses for statistical analyses. Results: On unprocessed images, R1 and R2 detected 232/310 nodules (R1: 114 GGN, 118 PSN) and 255/310 nodules (R2: 122 GGN, 133 PSN), respectively (P>0.05). On AI-VS images, they detected 249/310 nodules (119 GGN, 130 PSN) and 277/310 nodules (128 GGN, 149 PSN), respectively (P≥0.12). When compared to the SOR, accuracy (AUC) for detection of PSN on the AI-VS images (AUC 0.80–0.81) was greater than on the unprocessed images (AUC 0.70–0.76). AI-VS images enabled detection of solid components in five nodules deemed as GGN on the unprocessed images. Accuracy of AI-AD was lower than both the radiologists (AUC 0.60–0.72). Conclusions: AI-VS improved the detection and classification of SSN into GGN and PSN on LDCT of the chest for the two radiologist (R1 and R2) readers.