基于机器学习的 MRI 和 CT 图像术前功能性肝储备的放射组学分析。
Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image.
机构信息
Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Institute for Digital Medicine and Computer-assisted Surgery in Qingdao University, Qingdao University, Qingdao, China.
出版信息
BMC Med Imaging. 2023 Jul 17;23(1):94. doi: 10.1186/s12880-023-01050-1.
OBJECTIVE
The indocyanine green retention rate at 15 min (ICG-R15) is a useful tool to evaluate the functional liver reserve before hepatectomy for liver cancer. Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients.
METHODS
A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n = 133, MR: n = 78) and a test dataset (CT: n = 57, MR: n = 34). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation.
RESULTS
A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC = 0.917 and ACC = 0.882 when the threshold was set as ICG-R15 = 10%. When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.961 and ACC = 0.941. For CT groups, the classifier XGBoost performed best when ICG-R15 = 10% with AUC = 0.822 and ACC = 0.842. When ICG-R15 = 20%, classifier SVM performed best with AUC = 0.860 and ACC = 0.842. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.938 and ACC = 0.965.
CONCLUSIONS
Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.
目的
吲哚菁绿 15 分钟滞留率（ICG-R15）是评估肝癌肝切除术前肝功能储备的有用工具。我们以 ICG-R15 为标准，研究了钆塞酸二钠增强肝脏磁共振成像（MRI）或对比增强计算机断层扫描（CT）图像的基于机器学习（ML）的放射组学模型在评估肝细胞癌（HCC）患者肝功能储备方面的能力。
方法
回顾性纳入 190 例 HCC 患者，其中 112 例患者同时接受 CT 和 MRI 检查，将其随机分为训练数据集（CT：n=133，MR：n=78）和测试数据集（CT：n=57，MR：n=34）。然后，从 Gd-EOB-DTPA MRI 和 CT 图像中提取放射组学特征。选择与 ICG-R15 分类相关的特征。使用 5 种 ML 分类器对 ML 模型进行研究。利用 95%置信区间（CI）的接受者操作特征（ROC）曲线的准确率（ACC）和曲线下面积（AUC）来评估 ML 模型的性能。
结果
分别从 MRI 和 CT 中提取了 107 种不同的放射组学特征。选择与 ICG-R15 分类为 10%、20%和 30%相关的特征。在 MRI 组中，当阈值设置为 ICG-R15=10%时，XGBoost 分类器的 AUC=0.917，ACC=0.882，表现最佳。当 ICG-R15=20%时，随机森林分类器的 AUC=0.979，ACC=0.882，表现最佳。当 ICG-R15=30%时，XGBoost 分类器的 AUC=0.961，ACC=0.941，表现最佳。对于 CT 组，当 ICG-R15=10%时，XGBoost 分类器的 AUC=0.822，ACC=0.842，表现最佳。当 ICG-R15=20%时，SVM 分类器的 AUC=0.860，ACC=0.842，表现最佳。当 ICG-R15=30%时，XGBoost 分类器的 AUC=0.938，ACC=0.965，表现最佳。
结论
基于 MRI 和 CT 的机器学习模型均被证明是评估肝功能储备的有价值的非侵入性方法。
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