Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction.

ABSTRACT:

Background

Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT.

Methods

The consecutive adult patients with noncontrast chest ULDCT (0.07-0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m² were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis.

Results

The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measur

SUBMITTER: Zhao K 

PROVIDER: S-EPMC9599467 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature