术前对比增强CT的影像组学分析在肝细胞癌微血管侵犯及预后分层中的应用
Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma.
作者信息
Xu Tingfeng, Ren Liying, Liao Minjun, Zhao Bigeng, Wei Rongyu, Zhou Zhipeng, He Yong, Zhang Hao, Chen Dongbo, Chen Hongsong, Liao Weijia
机构信息
Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People's Republic of China.
Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China.
出版信息
J Hepatocell Carcinoma. 2022 Mar 20;9:189-201. doi: 10.2147/JHC.S356573. eCollection 2022.
PURPOSE
Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT).
PATIENTS AND METHODS
HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed.
RESULTS
A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature.
CONCLUSION
The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.
目的
微血管侵犯（MVI）会损害肝细胞癌（HCC）患者的长期预后。我们旨在基于增强CT（CECT）的影像组学特征开发一种新型列线图，以预测MVI及患者的预后。
患者与方法
纳入接受根治性切除的HCC患者。从肿瘤区域提取影像组学特征，选择最佳的与MVI相关的影像组学特征并应用于构建影像组学特征（Rad评分）。根据逻辑回归创建预测模型并进行评估。通过q-PCR对随机选择的HCC患者进行生物标志物分析。分析生物标志物与影像组学特征之间的相关性。
结果
共纳入421例HCC患者。从肿瘤区域提取了总共1962个影像组学特征，11个最佳的与MVI相关的影像组学特征在训练队列和验证队列中的曲线下面积（AUC）分别为0.796和0.810，显示出良好的预测能力。天冬氨酸转氨酶（AST）、肿瘤数量、甲胎蛋白（AFP）水平和影像组学特征是MVI的独立危险因素。将这四个因素整合到新型列线图中，命名为CRM，在预测MVI方面，训练队列中的AUC为0.767，验证队列中的AUC为0.793，在单独的影像组学特征和临床模型中表现最佳。列线图校准良好，决策曲线分析显示具有良好的临床价值，并且可以将患者分为复发和死亡的高风险或低风险亚组。此外，通过q-PCR分析了基因BCAT1、DTGCU2、DOCK3，血清AFP被确定与影像组学特征有显著关联。
结论
新型列线图在术前预测MVI概率方面表现良好，可能指导临床决策。
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