Qianbiao Gu1, Zhichao Feng2, Xiaoli Hu3, Mengtian Ma2, Mwajuma Mustafa Jumbe4, Haixiong Yan2, Peng Liu5, Pengfei Rong2. 1. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013; Department of Radiology, People's Hospital of Hunan Province, First Affiiated Hospital, Hunan Normal University, Changsha 410002, China. 2. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China. 3. First Hospital of Hunan University of Chinese Medicine, Changsha 410007, China. 4. Department of Radiology, Muhimbili National Hospital, Dar es Salaam 65000, Tanzania. 5. Department of Radiology, People's Hospital of Hunan Province, First Affiiated Hospital, Hunan Normal University, Changsha 410002, China.
Abstract
OBJECTIVE: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63. Methods: A total of 245 NSCLC patients who underwent CT scans were retrospectively included. All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination. Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images. The Lasso-logistic regression model was used to select features and develop radiomics signature, subjective image features model, and combined diagnostic model. The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve, and compared with Delong test. Results: Of the 245 patients, 96 were P63 positive and 149 were P63 negative. The subjective image feature model consisted of 6 image features. Through feature selection, the radiomics signature consisted of 8 radiomics features. The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755, respectively, without a significant difference (P>0.05). The combined diagnostic model showed the best predictive power (AUC=0.817, P<0.01). Conclusion: The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63. The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model, which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.
OBJECTIVE: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63. Methods: A total of 245 NSCLCpatients who underwent CT scans were retrospectively included. All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination. Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images. The Lasso-logistic regression model was used to select features and develop radiomics signature, subjective image features model, and combined diagnostic model. The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve, and compared with Delong test. Results: Of the 245 patients, 96 were P63 positive and 149 were P63 negative. The subjective image feature model consisted of 6 image features. Through feature selection, the radiomics signature consisted of 8 radiomics features. The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755, respectively, without a significant difference (P>0.05). The combined diagnostic model showed the best predictive power (AUC=0.817, P<0.01). Conclusion: The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63. The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model, which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.