Zhang Chao, Zhong Hai, Zhao Fang, Ma Zhen-Yu, Dai Zheng-Jun, Pang Guo-Dong
Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China.
Department of Radiology, Qilu Hospital of Shandong University, Jinan 250014, Shandong Province, China.
World J Gastrointest Oncol. 2024 Mar 15;16(3):857-874. doi: 10.4251/wjgo.v16.i3.857.
Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC).
To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC.
We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model's performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram.
The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.
This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.
最近，包绕肿瘤细胞簇的血管（VETC）被认为是一种独特的肿瘤血管生成模式，它能够以独立于侵袭的方式促进整个肿瘤细胞簇进入血液循环，并且被视为肝细胞癌（HCC）预后不良的独立危险因素。
开发并验证一种基于对比增强计算机断层扫描（CECT）的术前列线图，以预测HCC中VETC+的存在情况。
我们回顾性评估了190例经病理证实的HCC患者，这些患者在两个医疗中心接受了CECT扫描和针对分化簇34的免疫化学染色。在门静脉期对肿瘤内和肿瘤周围区域进行了放射组学分析。提取了识别VETC+ HCC所需的放射组学特征，并利用机器学习算法在训练集中建立了放射组学模型。该模型的性能在两个独立的测试集上进行了验证。采用受试者操作特征（ROC）分析来比较三个模型在训练集和测试集上预测HCC的VETC状态的识别性能。然后使用预测性最强的模型构建一个整合了独立临床放射学特征的放射组学列线图。采用ROC和决策曲线分析来评估临床放射学特征、放射组学特征和放射组学列线图的性能特征。
该研究纳入了来自两个独立中心的190名个体，其中大多数为男性（81%），中位年龄为57岁（四分位间距：51 - 66岁）。从肿瘤内和肿瘤周围区域选择的联合放射组学特征在训练集和两个测试集中的曲线下面积（AUC）分别为0.825、0.788和0.680。总共选择了13个特征来构建Rad评分。结合临床放射学和联合放射组学特征的列线图能够在所有三个数据集中准确预测VETC+，AUC值分别为0.859、0.848和0.757。决策曲线分析表明，放射组学列线图在临床上比临床放射学特征和联合放射组学模型更有用。
本研究证明了基于CECT的放射组学列线图在识别VETC+ HCC方面的潜在效用，该列线图纳入了临床放射学特征和联合放射组学特征。
