实用肝脏病杂志 ›› 2026, Vol. 29 ›› Issue (3): 425-428.doi: 10.3969/j.issn.1672-5069.2026.03.027

• 肝癌 • 上一篇    下一篇

增强CT纹理参数预测肝细胞癌病理学分化程度价值研究*

魏雨梦, 韩高飞, 高潘   

  1. 723000 陕西省汉中市 西安交通大学医学部附属三二○一医院医学影像科(魏雨梦,高潘);铜川市人民医院医学影像科(韩高飞)
  • 收稿日期:2025-11-04 出版日期:2026-05-10 发布日期:2026-05-18
  • 通讯作者: 韩高飞,E-mail:48489725@qq.com
  • 作者简介:魏雨梦,女,29岁,大学本科,住院医师。E-mail:wym1996hz@163.com
  • 基金资助:
    *陕西省科技厅重点研发计划项目(编号:2021SF-044)

Enhanced CT texture parameters in predicting pathological tumor cell differentiation in patients with single-centered hepatocellular carcinoma

Wei Yumeng, Han Gaofei, Gao Pan   

  1. Department of Radiology, 3201th Hospital, Affiliated to Xi'an Jiaotong University Medical School, Hanzhong 723000, Shaanxi Province, China
  • Received:2025-11-04 Online:2026-05-10 Published:2026-05-18

摘要: 目的 探讨应用增强CT纹理参数预测肝细胞癌(HCC)细胞病理学分化程度的价值。方法 2023年1月~2025年3月我院收治的86例单发HCC患者,术前均行增强CT扫描检查,应用CT Kinetics纹理参数分析软件记录CT平均值、峰度、偏度、能量和熵值。经组织病理学检查诊断及细胞分化分级。采用多因素Logistic回归分析影响肿瘤细胞低分化的因素,应用受试者工作特征(ROC)曲线评估预测效能。结果 在86例HCC患者中,病理学检查诊断细胞低分化27例(31.4%),中高分化59例(68.6%);低分化组肿瘤CT平均值、峰度、偏度和熵值分别为(88.7±10.3)、(5.1±0.6)、(1.4±0.5)和(2.4±0.4),均显著高于中高分化组【分别为(75.1±8.2)、(4.6±0.7)、(0.9±0.3)和(1.9±0.3),P<0.05】,而能量为(4.1±0.3)×106,显著低于中高分化组【(4.9±0.6)×106,P<0.05】;多因素Logistic回归分析显示,峰度(OR=1.844,95%CI:1.020~3.333)、偏度(OR=1.684,95%CI:1.029~2.754)和熵值(OR=1.824,95%CI:1.091~3.048)为HCC细胞低分化的独立影响因素(P<0.05);ROC曲线分析显示,肿瘤CT峰度、偏度和熵值联合预测肿瘤细胞低分化的曲线下面积(AUC)为0.919,其灵敏度为88.9%,特异度为94.9%,显著优于单一指标预测(P<0.05)。结论 应用增强CT纹理参数大致可以预测HCC细胞病理学分化程度,其临床价值还需要进一步探讨。

关键词: 肝细胞癌, 细胞分化, 增强CT扫描, 纹理分析, 预测

Abstract: Objective The aim of this study was to explore feasibility of enhanced CT texture parameters in predicting pathological tumor cell differentiation in patients with single-centered hepatocellular carcinoma (HCC). Methods 86 patients with single-centered HCC were encountered in our hospital between January 2023 and March 2025, and all patients received enhanced CT scan before surgery to record average CT value, kurtosis, skewness, energy and entropy in regions of interest. Tumor cell differentiation was determined by pathological exam. Univariate and multivariate Logistic regression analyses were applied to identify the influencing factors of poorly differentiated HCC, and the receiver operating characteristic (ROC) curves were drawn to evaluate the predictive efficacy of enhanced CT texture parameters for the degree of pathological differentiation of tumor cells. Results Of the 86 patients with solitary HCC, histo-pathological examination diagnosed poorly differentiated tumors in 27 cases (31.4%) and moderately to well-differentiated tumors in 59 cases (68.6%); the average CT value, kurtosis, skewness and entropy in the poor differentiation group were(88.7±10.3), (5.1±0.6), (1.4±0.5) and (2.4±0.4), all much higher than [(75.1±8.2), (4.6±0.7), (0.9±0.3) and (1.9±0.3), respectively, P<0.05], while energy was(4.1±0.3)×106, much lower than [(4.9±0.6)×106,P<0.05] in moderately to well-differentiation group; multivariate Logistic regression analysis showed that kurtosis(OR=1.844, 95%CI:1.020-3.333), skewness(OR=1.684, 95%CI:1.029-2.754) and entropy(OR=1.824, 95%CI:1.091-3.048) were all the independent impacting factors for poor differentiation(P<0.05); ROC analysis demonstrated that the AUC was 0.919, with sensitivity of 88.9% and specificity of 94.9%, when CT kurtosis was combined with skewness and entropy in predicting poor differentiation of tumor cells, much superior to any single parameter doing (P<0.05). Conclusion We tentatively recommend enhanced CT texture parameters for prediction of tumor cell differentiation in patients with single-centered HCC, which needs further clinical verification.

Key words: Hepatocellular carcinoma, Cell differentiation, Enhanced CT scan, CT texture parameters, Prediction