Journal article
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(4), pp.1134-1143
04/01/2021
DOI: 10.21037/qims-20-630
PMCID: PMC7930659
PMID: 33816155
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.
Details
- Title: Subtitle
- Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography
- Creators
- Ramandeep Singh - Massachusetts General HospitalMannudeep K. Kalra - Massachusetts General HospitalFatemeh Homayounieh - Massachusetts General HospitalChayanin Nitiwarangkul - Massachusetts General HospitalShaunagh McDermott - Massachusetts General HospitalBrent P. Little - Massachusetts General HospitalInga T. Lennes - Harvard UniversityJo-Anne O. Shepard - Massachusetts General HospitalSubba R. Digumarthy - Massachusetts General Hospital
- Resource Type
- Journal article
- Publication Details
- Quantitative imaging in medicine and surgery, Vol.11(4), pp.1134-1143
- DOI
- 10.21037/qims-20-630
- PMID
- 33816155
- PMCID
- PMC7930659
- NLM abbreviation
- Quant Imaging Med Surg
- ISSN
- 2223-4292
- eISSN
- 2223-4306
- Publisher
- AME PUBLISHING COMPANY
- Number of pages
- 10
- Language
- English
- Date published
- 04/01/2021
- Academic Unit
- Radiology
- Record Identifier
- 9984697624802771
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