Original Article
CT-based radiomics signature for the stratification of N2 disease risk in clinical stage I lung adenocarcinoma
Abstract
Background: Risk stratification of N2 disease is vital for selecting candidates to receive invasive mediastinal staging modalities. In this study, we aimed to stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma using radiomics analysis.
Methods: Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using PyRadiomics, 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset.
Results: The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77–0.86) and 0.69 (0.63–0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71–0.92) and 0.64 (0.52–0.75), respectively, in the validation dataset.
Conclusions: The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.
Methods: Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using PyRadiomics, 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset.
Results: The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77–0.86) and 0.69 (0.63–0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71–0.92) and 0.64 (0.52–0.75), respectively, in the validation dataset.
Conclusions: The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.
