Benign‐malignant classification of pulmonary nodules by low‐dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

Abstract

Abstract Background Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. Methods The subjects' demographic data, baseline examination results, and annual follow‐up low‐dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. Results A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%–0.46%, which was lower than the baseline level except the follow‐up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re‐examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67–0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. Conclusions It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow‐up.

Keywords

• cancer screening
• health examination
• low‐dose computed tomography
• lung cancer
• opportunistic screening
• pulmonary nodules