Prediction of surgical outcomes in ovarian cancer based on CT imaging

This study aims to investigate the commonly used prediction models for the outcomes of ovarian cancer surgery. The study will select 71 cases from the TCGA-OV dataset and divide them into a training set (n=51) and a validation set (n=20) at a ratio of 7:3. The project is based on PyRadiomics Python3.0.1 and extracts 107 basic image features from the image group data, including first-order statistics, shape, gray-level co-occurrence matrix, gray-level size zone matrix, GLRLM, etc. On this basis, the LASSO algorithm is used for feature dimensionality reduction. In building the model, a fuzzy matrix is used to analyze multiple evaluation indicators, including the area under the receiver operating characteristic (ROC) curve (AUC). This study hopes to provide more precise diagnostic and treatment plans for patients, with the goal of achieving better treatment outcomes.