Abstract
Objectives
Limited surgery has received increasing attention to minimize damage and preserve more functional lung tissue. However, invasive pathological features including occult lymph node metastasis, visceral pleural invasion, lymphovascular invasion and tumor spread through air spaces may become risk factors for prognosis after limited surgery. The aim of this study was to unitedly predict these invasive pathological features based on computed tomography (CT) radiomics in patients with early stage non-small cell lung cancer (NSCLC).
Methods
From January 2016 to February 2023, 910 patients with clinical stage IA-IIA NSCLC underwent resection and were divided into training and validation group based on different institution. Radiomics features were extracted by the PyRadiomics software after tumor lesion segmentation and screened by spearman correlation analysis, minimum redundancy maximum relevance and the least absolute shrinkage and selection operator regression analysis. Univariate analysis followed by multivariable logistic regression were performed to estimate the independent predictors. A predictive model was established with visual nomogram and external validation, and evaluated in terms of area under the receiver operating characteristic curve (AUC).
Results
225 patients had invasive pathological features (33.2%), and four independent predictors were identified: larger consolidation diameter (p = 0.032), pleural attachment (p = 0.013), texture (p < 0.001) and Rad-score (p < 0.001). The combined model showed good calibration with an AUC of 0.815, compared with 0.778 and 0.691 when radiomics or traditional CT features were used alone. For the validation group, the AUC was 0.792, compared with 0.745 and 0.701 in radiomics or traditional CT features model.
Conclusion
Our predictive model can non-invasively assess the risk of invasive pathological features in patients with clinical stage IA-IIA NSCLC, enable surgeons perform more reasonable and individualized treatment choices.