Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CTopen access
- Authors
- Yoo, Seung-Jin; Park, Young Sik; Choi, Hyewon; Kim, Da Som; Goo, Jin Mo; Yoon, Soon Ho
- Issue Date
- Feb-2024
- Publisher
- Public Library of Science
- Citation
- PLoS ONE, v.19, no.2
- Journal Title
- PLoS ONE
- Volume
- 19
- Number
- 2
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72987
- DOI
- 10.1371/journal.pone.0297390
- ISSN
- 1932-6203
- Abstract
- Purpose To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. Results DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). Conclusion DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images. © 2024 Yoo et al.
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