Yu Ting-Yu, Zhan Ze-Juan, Lin Qi, Huang Zhen-Huan
Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian Province, China.
World J Clin Cases. 2024 Aug 26;12(24):5568-5582. doi: 10.12998/wjcc.v12.i24.5568.
Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.
To construct a radiomics model that can accurately predict the expression in HCC.
Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.
and were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between expression and drug sensitivity. Elevated expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting expression. The Radscore was higher in the group with high expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and expression in predicting the overall survival of HCC patients.
plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted expression and exhibited strong correlation with HCC prognosis.
肝细胞癌（HCC）是最常见的肝癌亚型。目前HCC的主要治疗策略包括肝移植和手术切除。然而，这些方法往往产生不尽人意的结果，导致许多患者预后不良。这凸显了迫切需要识别和评估能够改善HCC患者预后和生存率的新型治疗靶点。
构建一个能够准确预测HCC中表达的放射组学模型。
从公共数据库中获取基因表达、临床参数、HCC相关放射组学和成纤维细胞相关基因。开发了一个基因模型，并对其临床疗效进行统计学评估。对鉴定出的枢纽基因进行药物敏感性分析。提取放射组学特征并采用机器学习算法生成与枢纽基因相关的放射组学模型。使用列线图来说明计算出的Radscore和枢纽基因在HCC患者预后背景下的预后意义。
和是HCC预后的独立预测因子，并被用于构建预测基因模型。该模型在诊断HCC方面表现出强大性能，并预测预后不良。观察到表达与药物敏感性之间呈负相关。表达升高与较差的预后相关，其在HCC中的诊断价值超过风险模型。使用逻辑算法开发的放射组学模型在预测表达方面也显示出更高的效率。高表达组的Radscore更高。构建了列线图以直观展示放射组学模型和表达在预测HCC患者总生存方面的重要作用。
在诊断HCC和治疗疗效方面发挥重要作用。使用逻辑算法开发的放射组学模型有效预测了表达，并与HCC预后表现出强相关性。
