Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China.
BMC Cancer. 2023 Sep 12;23(1):858. doi: 10.1186/s12885-023-11386-0.
Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment.
Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes.
Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively.
The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment.
肝细胞癌（HCC）的降期使得超出标准的患者有机会接受肝移植（LT）并获得更好的治疗效果。因此，预测治疗预后的方法是当务之急。本研究旨在构建一个包含临床信息和放射组学特征的综合框架，以准确预测降期治疗的预后。
具体来说，采用增强 CT 的三维（3D）肿瘤分割方法提取病变的空间信息。然后，计算分割区域内的放射组学特征。结合放射组学特征和临床数据，进行特征选择，以提高模型的稳健性和通用性。最后，采用支持向量机（SVM）算法建立预测 HCC 降期结果的分类模型。
在此，对三种不同的模型进行了对比研究：基于放射组学特征的模型（R 模型）、基于临床特征的模型（C 模型）和基于放射组学联合临床特征的模型（R-C 模型）。三个模型的平均准确率分别为 0.712、0.792 和 0.844，三个模型的平均接收者操作特征曲线（AUROC）下面积分别为 0.775、0.804 和 0.877。
新型实用的 R-C 模型能准确预测降期结果，可用于指导 HCC 降期至 LT 治疗。
