A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma - Zha, Xinyi; Liu, Yuanqing; Ping, Xiaoxia; Bao, Jiayi; Wu, Qian; Hu, Su; Hu, Chunhong (Frontiers in Oncology, 2022)
Abstract Objectives: To develop and validate a nomogram model based on radiomic features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma Methods: A total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomic features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomic features and construct radscore. A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA). Results: A total of 1316 radiomic features were extracted. A radiomic signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radiomic score (Radscore) between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomic features showed improved specificity, accuracy and discrimination for predicting VPI, compared to the radiomic model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram has clinical features beyond the traditional clinical and radiomic features. Conclusion: A nomogram model combining radiological and clinical features is effective for non-invasively prediction of VPI in patients with lung adenocarcinoma.