Develop and Validate a Preoperative Clinical Radiological Nomogram Based on Intratumoral and Peritumoral CT Radiomics to Predict Pathology Invasiveness of Pulmonary Nodules
Objective To develop a backpropagation neural network(BPNN)deep learning model based on the focal and peritumoral imaging features of pulmonary nodules,and combine CT imaging features and clinical information to construct a nomogram model for predicting the infiltration of pulmonary nodules.Methods 242 cases of GGN were retrospectively collected and divided into glandular prodromal disease(AAH/AIS)and invasive lung adenocarcinoma(MIA/IAC).The imaging signs and clinical features of the two groups were compared.Lung nodules and peritumoral target areas were characterized from lung CT images,and the image omics features were screened through single-factor rank sum test and correlation analysis.The back propagation neural network deep learning algorithm was used to construct a prediction model,and the predictive performance of the model was evaluated by area under receiver Operating characteristic(ROC)curve(AUC),and the model was generalized to verify in external validation cohort.Results The AUC of BPNN model in training cohort,internal validation cohort and external validation cohort were 0.883(95％CI:0.830-0.929),0.854(95％CI:0.786-0.909)and 0.854(95％CI:0.786-0.909),respectively.Univariate and multivariate analysis showed that crescent sign,CT value and GGN length diameter were independent risk factors for predicting pulmonary nodule invasion(P＜0.05).The AUC of the constructed clinical model in training cohort,internal validation cohort and external validation cohort were 0.889(95％CI:0.835-0.934),0.778(95％CI:0.668-0.879)and 0.901(95％CI:0.856-0.940),respectively.Combined with the BPNN model and the clinical model,the AUC of the model in training cohort,internal validation cohort and external validation cohort were 0.952(95％CI:0.920-0.977),0.891(95％CI:0.807-0.959)and 0.939(95％CI:0.899-0.968),respectively.Compared with the other two models,the combined model demonstrated a stronger model performance in evaluating pulmonary nodule pathologic infiltration.Conclusion The nomogram model based on tumor foci and peritumoral radiomic features combined with clinical-radiological information showed good predictive performance,and the combined model was well validated by generalization in external cohort,which could provide reference for clinical diagnosis and treatment of pulmonary nodules.
万方数据
维普
