Radiomics in predicting tumor molecular marker P63 for non-small cell lung cancer / 中南大学学报(医学版)

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.

Radiomics and its advances in hepatocellular carcinoma / 中南大学学报(医学版)

Liver cancer is the second leading cause of cancer-related death worldwide,so early detection and prediction for response to treatment is of great benefit to hepatocellular carcinoma (HCC) patients.Currently,needle biopsy and conventional medical imaging play a significant and basic role in HCC patients' management,while those two approaches are limited in sample error and observerdependence.Radiomics can make up for this deficiency because it is an emerging non-invasive technic that is capable of getting comprehensive information relevant to tumor situation across spatial-temporal limitation.The basic procedure for radiomics includes image acquisition,region of interest segmentation and reconstruction,feature extraction,selection and classification,and model building and performance evaluation.The current advances and potential prospect of radiomics in HCC studies are involved in diagnosis,prediction for response to treatment,prognosis evaluation and radiogenomics.