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’st-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 from0.5929to0.9268, 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.
肝脏恶性肿瘤，特别是肝细胞癌和转移性肝癌，是导致癌症死亡的主要原因。目前，放射科医生对腹部计算机断层扫描图像中大量数据的利用仍显不足。本研究探讨了利用放射组学特征，通过机器学习技术区分肿瘤组织与正常肝组织的应用。研究纳入了94例患者的术前增强扫描图像，从每位患者影像数据的感兴趣区域中提取了共计1686个特征，这些特征被归类为一阶、二阶、高阶及形状统计特征。随后，采用方差阈值法、基于学生t检验的统计学显著变量选择以及套索回归进行特征筛选。研究使用了六种分类器来识别肿瘤与非肿瘤肝组织，包括随机森林、支持向量机、朴素贝叶斯、自适应提升、极限梯度提升以及逻辑回归。通过网格搜索进行超参数调优，并采用10折交叉验证方法。采用受试者工作特征曲线下面积评估模型性能，其值介于0.5929至0.9269之间，其中朴素贝叶斯分类器取得了最佳评分。提取的放射组学特征获得了良好的分类评分，该放射组学特征谱可作为肝脏肿瘤筛查的预后生物标志物。
Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images
肿瘤(癌症)患者之家