Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Eur Radiol. 2019 Sep;29(9):4648-4659. doi: 10.1007/s00330-018-5935-8. Epub 2019 Jan 28.
OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. RESULTS: The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. CONCLUSIONS: We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. KEY POINTS: • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.
目的：术前预测肝细胞癌（HCC）的微血管侵犯（MVI）对于手术策略的制定非常重要。本研究旨在基于钆塞酸二钠（Gd-EOB-DTPA）增强磁共振成像（MRI），建立并验证一种联合肿瘤内和肿瘤周围的放射组学模型，用于预测原发性 HCC 患者的 MVI。
方法：本研究纳入了一个训练队列（110 例 HCC 患者）和一个验证队列（50 例 HCC 患者）。所有患者均接受了术前 Gd-EOB-DTPA 增强 MRI 检查和根治性肝切除术。手动在 MRI 肝胆期图像上勾画肝脏病变的感兴趣区（VOI），包括肿瘤内和肿瘤周围区域，并提取和分析定量特征。在训练队列中，采用机器学习方法进行降维和提取特征的选择。
结果：在训练队列和验证队列中，MVI 阳性患者的比例分别为 38.2%和 40.0%。有监督机器学习选择了 10 个特征来建立 MVI 的预测模型。在训练队列和验证队列中，联合肿瘤内和肿瘤周围放射组学模型的受试者工作特征曲线（ROC）下面积（AUC）、敏感性、特异性分别为 0.85（95%置信区间（CI），0.77-0.93）、88.2%、76.2%和 0.83（95%CI，0.71-0.95）、90.0%、75.0%。
结论：我们评估了肿瘤内和肿瘤周围区域的定量 Gd-EOB-DTPA 增强 MRI 图像特征，并提供了一种有效的基于放射组学的 HCC 患者 MVI 预测模型，这可能有助于临床医生在手术前做出精确的治疗决策。
关键点：
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World J Gastroenterol. 2025-2-28
Contrast Media Mol Imaging. 2018-1-23
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