High-resolution computed tomography with 1,024-matrix for artificial intelligence-based computer-aided diagnosis in the evaluation of pulmonary nodules.

Abstract

Background: Computed tomography (CT) plays an important role in the diagnosis of lung nodules and early screening of lung cancer. The purpose of this study was to compare the efficacy of 1,024×1,024 matrix and 512×512 matrix in an artificial intelligence-based computer-aided diagnosis (AI-CAD) for evaluating lung nodules based on CT images.

Methods: This retrospective analysis included 344 patients from two hospitals between January 2020 and November 2023. CT images presenting lung nodules smaller than 30 mm were reconstructed using the 512×512 and 1,024×1,024 matrix. We evaluated image quality and AI-CAD detection of lung nodules. Image quality was subjectively scored using a 5-point Likert method and objectively assessed using image noise and signal-to-noise ratio (SNR). For lung nodules detection, we recorded the accuracy, precision, and recall of AI-CAD for detecting of different types and sizes of lung nodules.

Results: The 512×512 matrix's overall image subjective evaluation score was 3.63, whereas the 1,024×1,024 matrix's was 4.18, among 344 individuals with 4,319 lung nodules. The detection accuracy, precision, and recall of 512×512 and 1,024×1,024 for AI-CAD in all lung nodules were 91.63% vs. 98.32%, 95.68% vs. 98.32%, and 95.59% vs. 100% respectively. Solid, part-solid, and nonsolid nodule identification accuracy on 512 and 1,024 matrix were 91.30% vs. 98.34%, 94.63% vs. 98.50%, and 94.71% vs. 97.74%, respectively, and of <6 mm, 6-8 mm, and >8 mm nodules were 90.58% vs. 97.87%, 96.64% vs. 99.04% and 93.68% vs. 99.36%, respectively.

Conclusions: The 1,024 matrix performed significantly better than the 512 matrix in terms of overall subjective image quality and lung nodule AI-CAD detection rate.