Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis

Background: Limited surgery is deemed advantageous due to its potential to minimize damage and preserve a greater extent of functional lung tissue, contingent upon the invasiveness of lung adenocarcinoma (ADC). The aim of this study was to non-invasively predict the invasiveness of ground-glass opacity (GGO) predominant nodules presented on preoperative computed tomography (CT) of ADC patients with clinical stage Ia.

Methods: We constructed a primary cohort comprising 437 clinical stage Ia ADC patients from the Tianjin Medical University Cancer Institute and Hospital and utilized data from 135 patients from the Tianjin Medical University General Hospital for validation. Radiomics features were extracted by the PyRadiomics software and screened by spearman correlation analysis, minimum redundancy maximum relevance and the least absolute shrinkage and selection operator (LASSO) regression analysis. The radiomics score (Rad-score) formula was then created by linearly combining the selected features, using their regression coefficients as weights. Univariate analysis followed by multivariable logistic regression were performed to estimate the independent predictors. An initial univariate analysis was followed by a multivariable logistic regression to estimate independent predictors. Area under the curve (AUC) was calculated after the model established through visual nomogram and external validation.

Results: Three hundred and seventy-four patients were pathologically confirmed as invasive ADC (65.4%), and three independent predictors were identified: maximum consolidation diameter (P=0.02), texture (P=0.042) and Rad-score (P<0.001). The combined model showed good calibration with an AUC of 0.911 [95% confidence interval (CI): 0.872, 0.951], compared with 0.883 (95% CI: 0.849, 0.932; DeLong's test P=0.16) and 0.842 (95% CI: 0.801, 0.896; DeLong's test P<0.001) when radiomics or CT semantic features were used alone. Combined prediction model accuracy for validation group was 0.865 (95% CI: 0.816, 0.908), which is reasonable.

Conclusions: Our study has provided a non-invasive prediction tool based on radiomics and CT semantic characteristics that can accurately assess the quantitative risk associated with the invasiveness of GGO predominant ADC in clinical stage Ia.