Clinical predictive models for individualized treatment of hepatocellular carcinoma
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摘要: 肝细胞癌(HCC)往往伴有肝炎和肝硬化,根据其疾病分期、肿瘤部位及肝功能等情况进行精准治疗的要求很高。临床上多种诊疗方法均对HCC具有一定的疗效,故如何有效地对患者进行预后分析,从而选择合适的个体化治疗方案,成为亟需解决的问题。针对HCC的预后,有多种预测系统,其中列线图因能够较好地针对HCC患者进行个体化分析而备受关注。目前,根据不同类型的HCC已建立了多种列线图预测模型,这些模型纳入了一些临床和病理指标,如肿瘤标志物、肝功能、HBV指标、微血管癌栓等,其对不同患者计算出不同的风险评分,能够较好地预测预后。同时,可以根据不同的风险等级指导患者选择合适的治疗方式和术后抗复发治疗,从而达到个体化治疗的目的。Abstract: Hepatocellular carcinoma ( HCC) is one of the most common malignant tumors in China and has high incidence and mortality rates. HCC is often accompanied by hepatitis and liver cirrhosis, and therefore, there is high demand for precise treatment based on disease stage, tumor location, and liver function. Various diagnostic and treatment methods used in clinical practice have a certain therapeutic effect on HCC, and the issue of effective prognostic analysis and selection of individualized treatment regimen needs to be solved urgently. There are many predictive systems for the prognosis of HCC, among which nomogram can better help with the individualized analysis of HCC patients and has thus attracted more and more attention. At present, various nomographic predictive models have been established for different types of HCC; such models integrate some clinical and pathological indices, such as tumor markers, liver function, HBV indices, and microvascular tumor thrombus, and then risk score is calculated for each patient to predict prognosis. Meanwhile, clinicians can select the appropriate therapeutic method and postoperative anti-recurrence treatment based on the level of risk to achieve the goal of individualized treatment.
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Key words:
- carcinoma, hepatocellular /
- nomograms /
- therapy, computer-assisted
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