Molecular Imaging Visualization of Tumor Heterogeneity in Non-small Cell Lung Cancer

Arms (1)

1. type

   EXPERIMENTAL

   label

   8F-FDG PET/CT and PET/CT-guide targeted biopsy

   description

   Each subject receive a single intravenous injection of 18F-FDG PET/CT and PET/CT-guide targeted biopsy within the specified time.

   interventionNames

   1. Diagnostic Test: 18F-FDG PET/CT and PET/CT-guide targeted biopsy in another group of participants

Primary outcomes (1)

1. measure

   Radiomic feature selection and model establishment

   timeFrame

   3 years

   description

   In this study, the investigators first selected the features with significant differences between genes mutant and wild type in the training set using the Mann-Whitney U test, obtaining a total of 53 features with p value \< 0.05. Then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal predictive features among the 53 selected in the training set. The LASSO algorithm adds a L1 regularization term to a least square algorithm to avoid overfitting. A prediction model was established by logistic regression, and the radiomics signature score (rad-score) for each participant was calculated based on the selected discriminating radiomic features. The model performance was tested in the validation set. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the model performance in the training and validation sets.