Prognostic value of radiomics features combined with CT imaging signs in pleural invasion of peripheral non-small cell lung cancer

LIU Xinyue; PAN Jiawen; YING Haifeng; ZHOU Baohe; CHEN Chunmiao; WANG Zufei

Abstract：Objective To investigate the value of combining radiomic features with CT imaging findings in the prediction of pleural invasion in peripheral non-small cell lung cancer.Methods Clinical and imaging data of 398 patients with peripheral NSCLC confirmed by surgery and pathology were retrospectively collected.The patients were divided into VPI(-)group(209 cases)and VPI(+)group(189 cases)according to the pathological findings of pleural infiltration.CT signs of all patients were evaluated.The patients were randomly assigned to the training set and validation set in a ratio of 7:3.The prediction models,in terms of texture feature model,CT imaging feature model and joint prediction model,were constructed by random forest regres-sion analysis,and the diagnostic performance was evaluated by ROC curve.Results Comparative analysis of CT imaging signs showed that there were statistically significant differences in mean diameter,mean CT value,density,focus-pleural relationship(RAP)typing and pleural depression sign between the two groups(P<0.05),and multivariate regression analysis showed that mean tumor diameter,density,RAP type and lymph node metastasis were independent predictors of VPI.A total of 786 texture parameters were selected,and 12 texture features with predictive significance were identified through mRMR and LASSO feature analysis.RF regression analysis constructed predictive models.The AUC of the combined prediction model was significantly higher than that of the texture feature model and CT imaging feature model(0.915 vs 0.856 vs 0.852;0.887 vs 0.855 vs 0.827).Conclusion Radiomic features combined with CT imaging findings can effectively predict the presence of pleural invasion in patients with peripheral non-small cell lung cancer≤3.0 cm in diameter.