Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors

Gongbo Xue; Hongyan Liu; Xiaoyi Cai; Zhen Zhang; Shuai Zhang; Ling Liu; Bin Hu; Guohua Wang

doi:10.3389/fonc.2023.1167745

Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors

Front Oncol. 2023 Apr 5:13:1167745. doi: 10.3389/fonc.2023.1167745. eCollection 2023.

Authors

Gongbo Xue^{1

2}, Hongyan Liu¹, Xiaoyi Cai^{1

2}, Zhen Zhang¹, Shuai Zhang³, Ling Liu³, Bin Hu⁴, Guohua Wang¹

Affiliations

¹ Department of Radiology, Qingdao Municipal Hospital, Qingdao, China.
² Graduate School, Dalian Medical University, Dalian, China.
³ CT Imaging Research Center, GE Healthcare China, Shanghai, China.
⁴ Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Abstract

Objective: To evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients.

Methods: Sixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC).

Results: Different reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998.

Conclusions: Both ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased.

Keywords: computed tomography; deep learning; image reconstruction; liver; texture analysis.