Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT

• Published in: Journal of computer assisted tomography Vol. 48; no. 6; p. 943
• Main Authors: Zheng, Zhijuan, Liang, Yuying, Wu, Zhehao, Han, Qijia, Ai, Zhu, Ma, Kun, Xiang, Zhiming
• Format: Journal Article
• Language: English
• Published: United States 01.11.2024
• Subjects: Adult; Aged; Algorithms; Deep Learning; Female; Humans; Lung - diagnostic imaging; Lung Neoplasms - diagnostic imaging; Male; Middle Aged; Multiple Pulmonary Nodules - diagnostic imaging; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiomics; Reproducibility of Results; Retrospective Studies; Solitary Pulmonary Nodule - diagnostic imaging; Tomography, X-Ray Computed - methods
• ISSN: 1532-3145
• DOI: 10.1097/RCT.0000000000001634

The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.