基于增强 CT 的影像组学模型预测肝癌根治性切除术后早期复发。
Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection.
机构信息
Laboratory of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China.
Department of Clinical Laboratory, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin 541002, Guangxi Zhuang Autonomous Region, China.
出版信息
World J Gastroenterol. 2023 Jul 14;29(26):4186-4199. doi: 10.3748/wjg.v29.i26.4186.
BACKGROUND
Radical resection remains an effective strategy for patients with hepatocellular carcinoma (HCC). Unfortunately, the postoperative early recurrence (recurrence within 2 years) rate is still high.
AIM
To develop a radiomics model based on preoperative contrast-enhanced computed tomography (CECT) to evaluate early recurrence in HCC patients with a single tumour.
METHODS
We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection. First, the features from the portal venous and arterial phases of CECT were extracted based on the region of interest, and the early recurrence-related radiomics features were selected the least absolute shrinkage and selection operator proportional hazards model (LASSO Cox) to determine radiomics scores for each patient. Then, the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression. Finally, we evaluated the prediction performance of this model by multiple methods.
RESULTS
A total of 1915 radiomics features were extracted from CECT images, and 31 of them were used to determine the radiomics scores, which showed a significant difference between the early recurrence and nonearly recurrence groups. Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alpha-fetoprotein were independent indicators, and they were used to develop a combined model to predict early recurrence. The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74, respectively, while the C-indices were 0.712 and 0.674, respectively. The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities. Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences.
CONCLUSION
The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.
背景
根治性切除术仍然是治疗肝细胞癌（HCC）患者的有效策略。不幸的是，术后早期复发（2 年内复发）率仍然很高。
目的
开发一种基于术前增强 CT（CECT）的放射组学模型，以评估单个肿瘤 HCC 患者的早期复发。
方法
我们共纳入了来自两个中心的 402 名 HCC 患者，这些患者被诊断为单个肿瘤并接受了根治性切除术。首先，基于感兴趣区域提取 CECT 门静脉期和动脉期的特征，并通过最小绝对收缩和选择算子比例风险模型（LASSO Cox）选择与早期复发相关的放射组学特征，以确定每位患者的放射组学评分。然后，将临床病理数据结合起来，通过 Cox 回归建立预测早期复发的模型。最后，我们通过多种方法评估该模型的预测性能。
结果
从 CECT 图像中提取了 1915 个放射组学特征，其中 31 个特征用于确定放射组学评分，早期复发组和非早期复发组之间的评分存在显著差异。单因素和多因素 Cox 回归分析表明，放射组学评分和血清甲胎蛋白是独立指标，可用于建立联合模型以预测早期复发。训练集和验证集的受试者工作特征曲线下面积分别为 0.77 和 0.74，C 指数分别为 0.712 和 0.674。校准曲线和决策曲线分析表明具有良好的准确性和临床实用性。基于无复发生存和总生存的 Kaplan-Meier 曲线显示出显著差异。
结论
术前放射组学模型可有效预测单个肿瘤 HCC 患者的早期复发。
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