부산대학교 도서관

Simple Summary: Liver malignancies, particularly hepatocellular carcinoma, and metastases stand as prominent contributors to cancer mortality. Within abdominal computed tomography imaging, much of data remains underused by radiologists. Radiomics uses advanced image analysis to extract quantitative features from medical scans for deeper diagnosis, treatment, and prognosis insights. Machine learning algorithms enable analyzing these features, facilitating an automatic, rapid, and efficient medical management process. We used these algorithms to train models that can distinguish between healthy livers and those with tumors, as well as between malignant and benign tumors, using CT images from the electronic medical record of the Centre National Hospitalier Universitaire Hubert Koutoukou Maga (CNHU-HKM) in Benin. The high correlation scores suggest that the radiomics signature is a prognostic biomarker for hepatic tumor screening. Liver malignancies, particularly hepatocellular carcinoma and metastasis, stand as prominent contributors to cancer mortality. Much of the data from abdominal computed tomography images remain underused by radiologists. This study explores the application of machine learning in differentiating tumor tissue from healthy liver tissue using radiomics features. Preoperative contrast-enhanced images of 94 patients were used. A total of 1686 features classified as first-order, second-order, higher-order, and shape statistics were extracted from the regions of interest of each patient's imaging data. Then, the variance threshold, the selection of statistically significant variables using the Student's t-test, and lasso regression were used for feature selection. Six classifiers were used to identify tumor and non-tumor liver tissue, including random forest, support vector machines, naive Bayes, adaptive boosting, extreme gradient boosting, and logistic regression. Grid search was used as a hyperparameter tuning technique, and a 10-fold cross-validation procedure was applied. The area under the receiver operating curve (AUROC) assessed the performance. The AUROC scores varied from 0.5929 to 0.9268 , with naive Bayes achieving the best score. The radiomics features extracted were classified with a good score, and the radiomics signature enabled a prognostic biomarker for hepatic tumor screening. [ABSTRACT FROM AUTHOR]