Lack of incremental value of three-dimensional measurement in assessing invasiveness for lung cancer

OBJECTIVES

The aim of this study was to evaluate the performance of consolidation-to-tumour ratio (CTR) and the radiomic models in two- and three-dimensional modalities for assessing radiological invasiveness in early-stage lung adenocarcinoma.

METHODS

A retrospective analysis was conducted on patients with early-stage lung adenocarcinoma from Guangdong Provincial People's Hospital and Shenzhen People's Hospital. Manual delineation of pulmonary nodules along the boundary was performed on cross-sectional images to extract radiomic features. Clinicopathological characteristics and radiomic signatures were identified in both cohorts. CTR and radiomic score for every patient were calculated. The performance of CTR and radiomic models were tested and validated in the respective cohorts.

RESULTS

A total of 818 patients from Guangdong Provincial People's Hospital were included in the primary cohort, while 474 patients from Shenzhen People's Hospital constituted an independent validation cohort. Both CTR and radiomic score were identified as independent factors for predicting pathological invasiveness. CTR in two- and three-dimensional modalities exhibited comparable results with areas under the receiver operating characteristic curves and were demonstrated in the validation cohort (area under the curve: 0.807 vs 0.826, P = 0.059) Furthermore, both CTR in two- and three-dimensional modalities was able to stratify patients with significant relapse-free survival (P < 0.000 vs P < 0.000) and overall survival (P = 0.003 vs P = 0.001). The radiomic models in two- and three-dimensional modalities demonstrated favourable discrimination and calibration in independent cohorts (P = 0.189).

CONCLUSIONS

Three-dimensional measurement provides no additional clinical benefit compared to two-dimensional.

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

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.