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Abstract：
OBJECTIVE: 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.
METHODS: 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.
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.
CONCLUSIONS: The nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.
METHODS: 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.
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.
CONCLUSIONS: The nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.
摘要：
目的：肝硬化患者局灶性肝脏病变（FLL）的检测和表征具有挑战性。关于FLL的准确信息是其管理的关键，从保守方法到手术切除。我们试图开发一个包含临床风险因素的列线图，血液指标,和增强的计算机断层扫描（CT）成像结果，以预测肝硬化肝脏中FLL的性质。
方法：共纳入348例经手术证实的FLL。评估CT表现和临床资料。单因素分析中P<0.05的所有因素均纳入多因素分析。进行ROC分析，并根据多变量逻辑回归分析结果构建列线图。
结果：FLL为良性（n=79）或恶性（n=269）。Logistic回归评估了影响恶性肿瘤的独立因素。AFP（OR=10.547），动脉期增快（APHE）（OR=740.876），冲洗(OR=0.028)，卫星病变（OR=15.164），腹水（OR=156.241），和结节中的结节结构（OR=27.401）是恶性肿瘤的独立预测因子。联合预测因子在鉴别良恶性病变方面表现优异，AUC为0.959，灵敏度为95.24%，训练队列的特异性为87.5%，AUC为0.981，灵敏度为94.74%，试验队列中的特异性为93.33%。C指数为96.80%,和校准曲线显示了列线图预测与实际数据之间的良好一致性。
结论：列线图显示了对恶性肿瘤风险预测的出色辨别和校准，它可能有助于做出FLL治疗决定。
方法：共纳入348例经手术证实的FLL。评估CT表现和临床资料。单因素分析中P<0.05的所有因素均纳入多因素分析。进行ROC分析，并根据多变量逻辑回归分析结果构建列线图。
结果：FLL为良性（n=79）或恶性（n=269）。Logistic回归评估了影响恶性肿瘤的独立因素。AFP（OR=10.547），动脉期增快（APHE）（OR=740.876），冲洗(OR=0.028)，卫星病变（OR=15.164），腹水（OR=156.241），和结节中的结节结构（OR=27.401）是恶性肿瘤的独立预测因子。联合预测因子在鉴别良恶性病变方面表现优异，AUC为0.959，灵敏度为95.24%，训练队列的特异性为87.5%，AUC为0.981，灵敏度为94.74%，试验队列中的特异性为93.33%。C指数为96.80%,和校准曲线显示了列线图预测与实际数据之间的良好一致性。
结论：列线图显示了对恶性肿瘤风险预测的出色辨别和校准，它可能有助于做出FLL治疗决定。
