Machine learning model based on Gd-EOB-DTPA-enhanced MRI in predicting microvascular invasion of hepatocellular carcinoma
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摘要:
目的 探讨基于钆塞酸二钠增强MRI的机器学习模型预测肝细胞癌微血管侵犯(MVI)的价值。 方法 回顾性分析2017年1月~2020年12月接受钆塞酸二钠增强MR扫描的59例经病理证实为肝细胞癌患者的MRI图像资料及临床资料，依据术后病理结果分为MVI阴性组(n=34)及阳性组(n=25)。分别在肝胆特异期及表观弥散系数图像上测量得到信噪比及对比噪声比。采用主成分分析法对特征进行降维并构建支持向量机模型，采用ROC曲线及混淆矩阵评价模型的诊断效能。 结果 构建的支持向量机预测模型诊断MVI的曲线下面积为0.92(95% CI: 0.83, 0.95)，准确率为0.80，敏感度为0.64，特异性为0.91。 结论 基于钆塞酸二钠增强MRI构建的机器学习模型在肝细胞癌术前诊断MVI具有较好的应用价值。 Abstract:Objective To explore the effectiveness of a machine learning model based on Gd-EOB-DTPA enhanced MRI in predicting microvascular invasion (MVI) of hepatocellular carcinoma. Methods We retrospectively analyzed MRI images and clinical data from 59 patients who underwent Gd-EOB-DTPA enhanced imaging and were pathologically confirmed with hepatocellular carcinoma from January 2017 to December 2020. Based on histopathological results, the patients were divided into MVI-positive group and MVI-negative group. The signal noise ratio and contrast to noise ratio in the hepatobiliary-specific phase and apparent dispersion coefficient images were measured. Principal component analysis was used for feature dimension reduction, and a support vector machine model was developed. The diagnostic performance of the model was evaluated using ROC curves and a confusion matrix. Results The AUC value of the support vector machine model was 0.86 (95% CI: 0.83-0.95). The accuracy, sensitivity and specificity were 0.80, 0.64 and 0.91, respectively. Conclusion The machine learning model based on Gd-EOB-DTPA enhanced MRI has potential in predicting MVI before hepatocellular carcinoma surgery. -
图 1 术后病理为HCC MVI阳性患者的MRI图像
Figure 1. MRI images of a patient with postoperative pathology confirmed as HCC with MVI-positive. A: Liver and gallbladder specific phase image measurement schematic diagram; B: ADC map measurement schematic diagram.
图 2 术后病理为HCC MVI阴性患者的MR图像
Figure 2. MRI images of a patient with postoperative pathology confirmed as HCC with MVI-negative. A: Liver and gallbladder specific phase image measurement schematic diagram; B: ADC map measurement schematic diagram.
图 3 基于Gd-EOB-DTPA增强MRI构建的SVM模型预测MVI的ROC曲线
Figure 3. ROC curve for predicting MVI using the SVM model constructed based on Gd-EOB-DTPA enhanced MRI.
图 4 基于Gd-EOB-DTPA增强MRI构建的SVM模型的混淆矩阵
Figure 4. Confusion matrix of the SVM model constructed based on Gd-EOB-DTPA enhanced MRI.
表 1 MVI阴性组与MVI阳性组临床资料结果比较
Table 1. Comparison of clinical data between the MVI-negative group and the MVI-positive group.
Index MVI-negative group(n=34) MVI-positive group(n=25) t/U/χ2 P Age (years, Mean±SD) 59.7±10.3 61.2±9.7 -0.55 0.58 Gender [n(%)] 1.61 0.39 Male 32 21 Female 2 4 AFP [ng/mL, n(%)] 8.64 < 0.01 ≤20 24 8 > 20 10 17 Child-Pugh classification [n(%)] 0.13 0.91 A 31 23 B 3 2 C 0 0 Tumor diameter (cm, Mean±SD) 5.25±2.97 8.27±3.92 -3.20 < 0.01 AFP: Alpha-fetoprotein; MVI: Microvascular invasion. 
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