基于CT影像特征预测肝细胞肝癌微血管侵犯的列线图模型

A Nomogram Model for Predicting Hepatocellular Carcinoma with Microvascular Invasion Based on CT Imaging Features

摘要: 目的：探讨评估肝细胞肝癌(HCC)微血管侵犯(MVI)的临床指标及CT影像征象，并建立预测发生MVI风险的列线图模型。方法：回顾性分析160例经术后病理证实为HCC患者的CT影像特征及临床资料，并与病理结果进行对照分析，筛选出MVI相关的预测因素，应用列线图构建评分模型，并评估模型的预测能力。结果：筛选出肿瘤直径、肿瘤形态及低密度环征3个危险因素，并应用列线图构建评分模型。列线图模型的C-index = 0.703，95% CI：0.621~0.784，拟合优度检验(χ² = 3.433，自由度为8，P > 0.05)。Bootstrap自抽样法的内部验证校正C-index = 0.683。结论：由三个CT影像征象(肿瘤直径、肿瘤形态及低密度环征)构建的列线图模型在一定程度上可以预测HCC患者是否存在MVI，对HCC患者的临床治疗决策有一定的指导作用。

Abstract: Objective: To investigate and access the clinical indicators and CT imaging signs of microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and to establish a nomogram model to predict the risk of MVI. Methods: The CT image features and clinical data of 160 patients with postoperatively pathologically confirmed HCC were retrospectively analyzed, and compared with the pathological results, to screen out the predictive factors related to MVI, and the nomogram method was used to construct a scoring model, and evaluate the predictive ability of the model. Results: Three risk factors including tumor diameter, tumor morphology and hypodense halo were screened and the scoring model was constructed by applying the nomogram. The C-index of the model was 0.703 (95% CI: 0.621~0.784), and the goodness-of-fit test (χ² = 3.433, degrees of freedom 8, P > 0.05). Internal validation result of Bootstrap self-sampling method to corrected C-index was 0.683. Conclusion: The nomogram model constructed by the three CT imaging signs (tumor diameter, tumor morphology and hypodense halo) can predict the presence or absence of MVI in HCC patients to a certain extent, and has a certain guiding effect on the clinical treatment decision of HCC patients.