To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models. Primary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored. This retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before. Statistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model. Our results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone.

中文翻译：

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多模型成像机器学习鉴别早期肝细胞癌的研究

探讨基于CT和MR多相影像组学结合不同机器学习模型对肝细胞癌（HCC）和非肝细胞癌（non-HCC）鉴别诊断的价值，并比较不同影像组学模型之间的诊断效果。原发性肝癌是临床最常见的恶性肿瘤之一，肝细胞癌(HCC)是原发性肝癌最常见的亚型，约占原发性肝癌病例的90%。 HCC的明确诊断对于HCC患者的个体化治疗具有重要意义。然而，需要探索更复杂的诊断方式。这项回顾性研究纳入了 211 名肝脏病变患者：97 名 HCC 和 124 名非肝细胞癌 (non-HCC)，他们接受了 CT 和 MRI 检查。成像数据用于获取病变和放射组学感兴趣区域（ROI）的成像特征。结合提取的成像特征构建不同的放射组学模型。然后将临床数据和影像学特征与放射组学特征相结合来构建组合模型。使用支持向量机（SVM）、K近邻（KNN）、随机森林（RF）、极限梯度提升（XGBoost）、轻梯度提升机（LightGBM）、多层感知器（MLP）六种机器学习模型进行训练。采用五折交叉验证训练模型，并利用ROC曲线分析各模型的诊断效能并计算准确率。模型训练和功效测试如前所述。统计分析显示，一些临床数据（性别和合并肝硬化）和影像学特征（有包膜、动脉期显着强化、门静脉期快速轮廓、均匀密度/信号和合并脂肪变性）存在统计学差异（P < 0.001） ）。机器学习模型的结果表明KNN具有最佳的诊断效果。组合模型的结果显示SVM具有最佳的诊断效果，表明组合模型（准确度0.824）比仅放射组学模型具有更好的诊断效果。我们的结果表明，CT 和 MRI 的放射组学特征与机器学习模型相结合，可以对 HCC 和非 HCC（恶性、良性）进行鉴别诊断。双放射组学诊断模型具有更好的诊断效果。组合模型优于单独的放射组学模型。