目的:准确预测肝细胞癌(HCC)患者术后复发风险可能有助于个体化治疗策略。本研究旨在开发基于术前临床因素和多参数磁共振成像(MRI)特征的机器学习模型,以预测HCC切除术后1年的复发。
方法:对82例接受手术治疗的单发肝癌患者进行回顾性分析。所有患者术前均行加氧酸增强MRI检查。收集术前临床因素和MRI特征进行特征选择。应用最小绝对收缩和选择算子(LASSO)选择预测HCC术后1年复发的最佳特征。四种机器学习算法,多层感知(MLP),随机森林,支持向量机,和k最近的邻居,用于根据选定的特征构建预测模型。使用接收器工作特性(ROC)曲线来评估每个模型的性能。
结果:在入选患者中,32名患者在一年内复发,50没有。肿瘤大小,肿瘤周围性低,肝实质T1值降低率(ΔT1),使用LASSO选择甲胎蛋白(AFP)水平来建立机器学习模型。每个模型的曲线下面积(AUC)超过0.72。在模型中,MLP模型在AUC下表现出最佳性能,准确度,灵敏度,特异性分别为0.813、0.742、0.570和0.853。
结论:机器学习模型可以准确预测HCC患者术后1年复发,这可能有助于提供个性化治疗。
OBJECTIVE: Accurate prediction of recurrence risk after resction in patients with Hepatocellular Carcinoma (HCC) may help to individualize therapy strategies. This study aimed to develop machine learning models based on preoperative clinical factors and multiparameter Magnetic Resonance Imaging (MRI) characteristics to predict the 1-year recurrence after HCC resection.
METHODS: Eighty-two patients with single HCC who underwent surgery were retrospectively analyzed. All patients underwent preoperative gadoxetic acidenhanced MRI examination. Preoperative clinical factors and MRI characteristics were collected for feature selection. Least Absolute Shrinkage and Selection Operator (LASSO) was applied to select the optimal features for predicting postoperative 1-year recurrence of HCC. Four machine learning algorithms, Multilayer Perception (MLP), random forest, support vector machine, and k-nearest neighbor, were used to construct the predictive models based on the selected features. A Receiver Operating Characteristic (ROC) curve was used to assess the performance of each model.
RESULTS: Among the enrolled patients, 32 patients experienced recurrences within one year, while 50 did not. Tumor size, peritumoral hypointensity, decreasing ratio of liver parenchyma T1 value (ΔT1), and α-fetoprotein (AFP) levels were selected by using LASSO to develop the machine learning models. The area under the curve (AUC) of each model exceeded 0.72. Among the models, the MLP model showed the best performance with an AUC, accuracy, sensitivity, and specificity of 0.813, 0.742, 0.570, and 0.853, respectively.
CONCLUSIONS: Machine learning models can accurately predict postoperative 1-year recurrence in patients with HCC, which may help to provide individualized treatment.