Incorporating Inflammatory Markers and Clinical Indicators into a Predictive Model of Single Small Hepatocellular Carcinoma Recurrence After Primary Locoregional Treatments

IF 4.2 3区 医学 Q2 ONCOLOGY
W. Qiao, Yiqi Xiong, Kang Li, Ronghua Jin, Yonghong Zhang
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Abstract

Purpose We explored the role of tumor size and number in the prognosis of HCC patients who underwent ablation and created a nomogram based on machine learning to predict the recurrence. Patients and Methods A total of 990 HCC patients who underwent transcatheter arterial chemoembolization (TACE) combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were prospectively enrolled, including 478 patients with single small HCC (S-S), 209 patients with single large (≥30mm) HCC (S-L), 182 patients with multiple small HCC (M-S), and 121 patients with multiple large HCC (M-L). S-S patients were randomized in a 7:3 ratio into the training cohort (N=334) and the validation cohort (N=144). Lasso-Cox regression analysis was carried out to identify independent risk factors, which were used to construct a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Patients in the training and validation cohorts were divided into low-risk, intermediate-risk, and high-risk groups based on the risk scores of the nomogram. Results The median recurrence-free survival (mRFS) in S-S patients was significantly longer than the S-L, M-S, and S-L patients (P<0.0001). The content of the nomogram includes age, monocyte-to-lymphocyte (MLR), gamma-glutamyl transferase-to-lymphocyte (GLR), International normalized ratio (INR), and Erythrocyte (RBC). The C-index (0.704 and 0.71) and 1-, 3-, and 5-year AUCs (0.726, 0.800, 0.780, and 0.752, 0.761, 0.760) of the training and validation cohorts proved the excellent predictive performance of the nomogram. Calibration curves the DCA curves showed that the nomogram had good consistency and clinical utility. There were apparent variances in RFS between the low-risk, intermediate-risk, and high-risk groups (P<0.0001). Conclusion S-S patients who underwent ablation had the best prognosis. The nomogram developed and validated in the study had good predictive ability for S-S patients.
将炎症标记物和临床指标纳入原发性局部治疗后单个小肝细胞癌复发的预测模型中
目的 我们探讨了肿瘤大小和数量在接受消融术的 HCC 患者预后中的作用,并基于机器学习创建了一个预测复发的提名图。患者和方法 前瞻性纳入了2014年1月至2021年12月在北京佑安医院接受经导管动脉化疗栓塞(TACE)联合消融术的990例HCC患者,包括478例单发小HCC(S-S)患者、209例单发大HCC(≥30mm)(S-L)患者、182例多发小HCC(M-S)患者和121例多发大HCC(M-L)患者。S-S患者按7:3的比例随机分为训练组(334人)和验证组(144人)。通过 Lasso-Cox 回归分析确定了独立的风险因素,并以此构建了一个提名图。通过C指数、接收者操作特征曲线(ROC)、校准曲线和决策曲线分析(DCA)曲线评估了提名图的性能。根据提名图的风险评分,将训练组和验证组的患者分为低风险组、中风险组和高风险组。结果 S-S患者的中位无复发生存期(mRFS)明显长于S-L、M-S和S-L患者(P<0.0001)。提名图的内容包括年龄、单核细胞对淋巴细胞(MLR)、γ-谷氨酰转移酶对淋巴细胞(GLR)、国际正常化比值(INR)和红细胞(RBC)。训练队列和验证队列的 C 指数(0.704 和 0.71)以及 1、3 和 5 年的 AUC(0.726、0.800、0.780 和 0.752、0.761、0.760)证明了提名图的出色预测性能。DCA 曲线的校准曲线显示,提名图具有良好的一致性和临床实用性。低危、中危和高危组之间的 RFS 存在明显差异(P<0.0001)。结论 接受消融术的 S-S 患者预后最好。本研究开发并验证的提名图对 S-S 患者具有良好的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
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