Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

Rui Zhang; Huaiqiang Sun; Bojiang Chen; Renjie Xu; Weimin Li

doi:10.21037/jtd-21-80

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

J Thorac Dis. 2021 Jul;13(7):4156-4168. doi: 10.21037/jtd-21-80.

Authors

Rui Zhang¹, Huaiqiang Sun², Bojiang Chen¹, Renjie Xu³, Weimin Li¹

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.
² Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
³ Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.

Abstract

Background: Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

Methods: This study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model.

Results: Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model.

Conclusions: Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.

Keywords: Radiomics; lung cancer; risk model; solid nodules; subsolid nodules.