## 2 Materials and Methods
### Radiomics and Artificial Intelligence  
Radiomics approaches include texture analysis as a subset and compute hundreds of features to describe tumor characteristics. The tumor phenotype was quantified and images were converted into mineable data with high-dimensional features. We also extracted image features related to tumor intensity (histogram), shape, texture and wavelet (high or low-frequency feature).  
To extract image features from CT data, a binary mask is created by specifying the lesion area of the image as an ROI. Using the pyradomics library, we input the original image data and the generated binary mask to extract radiomics features for the lesion area of the original image. Radiomics feature extraction is the process of quantifying the imaging features of a specified region in image data through a mask into quantitative variables such as shape features, first-order features, and second-order features. Shape features are features related to the shape of the ROI specified by the mask, which are variables that quantify the size, shape, surface, and orientation of the image. The extracted shape features include the number of pixels contained within the ROI (volume), the number of surface pixels to measure the surface area (surface area), how close to spherical the ROI is (sphericity), and how elongated the shape of the ROI is (elongation). First-order features are features related to the distribution of pixel values within the ROI and are computed using a histogram of the frequencies of pixel values. These features quantify information such as the brightness, contrast, and sharpness of the image. Second-order features are features related to the spatial correlation of pixel values in the ROI of a medical image, and are calculated by constructing a matrix that represents the spatial relationship of each pair of pixels in the region of interest and performing a matrix product with the image data. It is expressed as Gray-Level Dependence Matrix (GLDM), Gray-Level Run Length Matrix (GLRLM). The features computed in this way can quantify and express information such as texture or pattern in the image.  
The extracted radiomics variables consist of 107 features, including shape features (14 features), first-order features (18 features), and second-order features including GLCM and GLDM (75 features). The authors added 5 clinical variables to this, for a total of 112 features used in the study.  
We conducted the training phase using retrospective CT data (100 patients, from January 2015 to February 2016) and performed the prediction phase using prospective data (57 patients) (Table 1). Radiomic features were extracted from CT images of the arterial phase. The location of the tumor was marked by the radiologist, and the tumor was segmented by the medical imaging engineer. Finally, the segmentation result was confirmed by the radiologist.  
We used radiomics to extract variables from the baseline and follow-up CT data (107 variables each). The variables extracted from the baseline and follow-up data were combined with clinical variables to create one dataset (112 variables in total, 98 cases). The model was trained using the scikit-survival framework, which was analyzed using the Random Forest Survival method. A total of 98 cases was used as the training set, and a prediction group of 48 cases was used as the test set. We used TensorFlow 2.6 framework.