Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.

This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8. In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.