التفاصيل البيبلوغرافية
| العنوان: |
Characteristics of study participants. |
| المؤلفون: |
Seung-Jin Yoo, Young Sik Park, Hyewon Choi, Da Som Kim, Jin Mo Goo, Soon Ho Yoon |
| سنة النشر: |
2024 |
| مصطلحات موضوعية: |
Medicine, Cell Biology, Biotechnology, Developmental Biology, Mental Health, Space Science, volume differences smaller, significantly higher signal, volumetric nodule assessments, volumetric nodule assessment, nodule volume differences, automatic nodule volumetry, two readers using, ct image quality, chest ct scans, 7 &# 8211, 01 &# 8211, 96 ± 0, 12 ± 0, 21 women ), nodules &# 8805, 6 &# 8211, ldct images compared, nodule sensitivities, scans reconstructed, compared subjectively, comparable quality, automatically using, 7 mm, 01 mgy |
| الوصف: |
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-V 50 ) 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-V 50 (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-V 50 (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 mm 3 ) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V 50 -LDCT images (-63.9 to 78.5 mm 3 ), 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. |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/Characteristics_of_study_participants_/25268969 |
| DOI: |
10.1371/journal.pone.0297390.t001 |
| الاتاحة: |
https://doi.org/10.1371/journal.pone.0297390.t001
https://figshare.com/articles/dataset/Characteristics_of_study_participants_/25268969 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.FD96D6A9 |
| قاعدة البيانات: |
BASE |