Wu Hongzhen, Wang Zihua, Liang Yingying, Tan Caihong, Wei Xinhua, Zhang Wanli, Yang Ruimeng, Mo Lei, Jiang Xinqing
Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Front Oncol. 2022 Jan 21;11:681489. doi: 10.3389/fonc.2021.681489. eCollection 2021.
The detection and characterization of focal liver lesions (FLLs) in patients with cirrhosis is challenging. Accurate information about FLLs is key to their management, which can range from conservative methods to surgical excision. We sought to develop a nomogram that incorporates clinical risk factors, blood indicators, and enhanced computed tomography (CT) imaging findings to predict the nature of FLLs in cirrhotic livers.
A total of 348 surgically confirmed FLLs were included. CT findings and clinical data were assessed. All factors with P < 0.05 in univariate analysis were included in multivariate analysis. ROC analysis was performed, and a nomogram was constructed based on the multivariate logistic regression analysis results.
The FLLs were either benign (n = 79) or malignant (n = 269). Logistic regression evaluated independent factors that positively affected malignancy. AFP (OR = 10.547), arterial phase hyperenhancement (APHE) (OR = 740.876), washout (OR = 0.028), satellite lesions (OR = 15.164), ascites (OR = 156.241), and nodule-in-nodule architecture (OR =27.401) were independent predictors of malignancy. The combined predictors had excellent performance in differentiating benign and malignant lesions, with an AUC of 0.959, a sensitivity of 95.24%, and a specificity of 87.5% in the training cohort and AUC of 0.981, sensitivity of 94.74%, and specificity of 93.33% in the test cohort. The C-index was 96.80%, and calibration curves showed good agreement between the nomogram predictions and the actual data.
The nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.
对肝硬化患者局灶性肝病变（FLLs）进行检测和特征描述具有挑战性。关于FLLs的准确信息是其管理的关键，管理方式可从保守方法到手术切除。我们试图开发一种列线图，纳入临床风险因素、血液指标和增强计算机断层扫描（CT）影像表现，以预测肝硬化肝脏中FLLs的性质。
共纳入348例经手术证实的FLLs。评估CT表现和临床数据。单因素分析中P<0.05的所有因素纳入多因素分析。进行ROC分析，并根据多因素逻辑回归分析结果构建列线图。
FLLs为良性（n = 79）或恶性（n = 269）。逻辑回归评估了对恶性肿瘤有正向影响的独立因素。甲胎蛋白（AFP）（比值比[OR]=10.547）、动脉期强化（APHE）（OR = 740.876）、廓清（OR = 0.028）、卫星灶（OR = 15.164）、腹水（OR = 156.241）和结节内结节结构（OR = 27.401）是恶性肿瘤的独立预测因素。联合预测因素在区分良性和恶性病变方面表现出色，在训练队列中的曲线下面积（AUC）为0.959，灵敏度为95.24%，特异度为87.5%；在测试队列中的AUC为0.981，灵敏度为94.74%，特异度为93.33%。一致性指数（C-index）为96.80%，校准曲线显示列线图预测与实际数据之间具有良好的一致性。
该列线图在恶性风险预测方面显示出优异的区分度和校准度，可能有助于做出FLLs的治疗决策。
