Radiomics technology for identifying early-stage lung adenocarcinomas suitable for sublobar resection

Abstract

Objective

Early-stage lung adenocarcinomas that are suitable for limited resection to preserve lung function are difficult to identify. Using a radiomics approach, we investigated the efficiency of voxel-based histogram analysis of 3-dimensional computed tomography images for detecting less-invasive lesions suitable for sublobar resection.

Methods

We retrospectively reviewed the medical records of 197 patients with pathological stage 0 or IA adenocarcinomas who underwent lung resection for primary lung cancer at our institution between January 2014 and June 2018. The lesions were categorized as either less invasive or invasive. We evaluated tumor volumes, solid volume percentages, mean computed tomography values, and variance, kurtosis, skewness, and entropy levels. We analyzed the relationships between these variables and pathologically less-invasive lesions and designed an optimal model for detecting less-invasive adenocarcinomas.

Results

Univariate analysis revealed seven variables that differed significantly between less invasive (n = 71) and invasive (n = 141) lesions. A multivariate analysis revealed odds ratios for tumor volumes (0.64; 95% confidence interval (CI), 0.46-0.89; P = .008), solid volume percentages (0.96; 95% CI, 0.93-0.99; P = .024), skewness (3.45; 95% CI, 1.38-8.65; P = .008), and entropy levels (0.21; 95% CI, 0.07-0.58; P = .003). The area under the receiver operating characteristic curve was 0.90 (95% CI, 0.85-0.94) for the optimal model containing these 4 variables, with 85% sensitivity and 79% specificity.

Conclusions

Voxel-based histogram analysis of 3-dimensional computed tomography images accurately detected early-stage lung adenocarcinomas suitable for sublobar resection.