Machine Learning-Based Model for Predicting Recurrence-Free Survival After Interventional Therapy in Malnourished Hepatocellular Carcinoma Patients

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-09-14 DOI:10.1002/cam4.71157

Ningning Lu, Chunwang Yuan, Bin Sun, Xiongwei Cui, Wenfeng Gao, Yonghong Zhang

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引用次数: 0

Abstract

Objective

This study intends to utilize machine learning approaches to screen out the crucial factors affecting the recurrence of hepatocellular carcinoma (HCC) patients with preoperative malnutrition after interventional therapy, and based on the identified factors, develop a nomogram for predicting the patients' 1-, 3-, and 5-year recurrence-free survival (RFS).

Methods

This study encompassed the clinical data of 512 malnourished (CONUT score ≥ 2) HCC patients who received the combination treatment of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) at Beijing You'an Hospital between January 2014 and January 2020. These patients were then randomly partitioned into training and validation cohorts at a 7:3 ratio. To investigate the factors influencing the post-treatment recurrence of malnourished HCC patients, methods such as random survival forest (RSF), eXtreme gradient boosting (XGBoost), and multivariate Cox regression analysis were employed. A nomogram was constructed based on the identified crucial factors to predict RFS in HCC patients. Subsequently, its performance was evaluated through Kaplan–Meier (KM) curves, receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

This study determined that GGT, APTT, age, and ALT are independent risk factors influencing recurrence in malnourished HCC patients. Based on the four risk factors, a nomogram for predicting RFS was effectively developed. The KM curve analysis showed that the nomogram could significantly distinguish between patient groups with varying recurrence risks. Furthermore, the nomogram's discriminative ability, accuracy, and decision-making efficacy were validated through the above-mentioned evaluation indicators, collectively suggesting its robust predictive performance.

Conclusions

We developed a nomogram that can predict the 1-, 3-, and 5-year RFS of malnourished HCC patients after undergoing the combination treatment; the constructed nomogram exhibited favorable predictive capabilities.

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基于机器学习的预测营养不良肝癌患者介入治疗后无复发生存的模型

目的本研究拟利用机器学习方法筛选影响肝细胞癌（HCC）术前营养不良患者介入治疗后复发的关键因素，并基于识别的因素，建立预测患者1年、3年、5年无复发生存（RFS）的nomogram。方法收集2014年1月至2020年1月在北京友安医院接受经动脉化疗栓塞（TACE）和射频消融（RFA）联合治疗的512例营养不良（CONUT评分≥2）HCC患者的临床资料。然后将这些患者按7:3的比例随机分为训练组和验证组。为探讨营养不良HCC患者治疗后复发的影响因素，采用随机生存森林（RSF）、极限梯度增强（XGBoost）、多因素Cox回归分析等方法。根据确定的关键因素构建nomogram来预测HCC患者的RFS。随后，通过Kaplan-Meier （KM）曲线、受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）对其性能进行评价。结果本研究确定GGT、APTT、年龄、ALT是影响营养不良HCC患者复发的独立危险因素。基于四种危险因素，建立了预测RFS的nomogram。KM曲线分析显示nomogram可以明显区分不同复发风险的患者组。此外，通过上述评价指标验证了nomogram的判别能力、准确性和决策效能，表明nomogram具有稳健的预测性能。结论：我们开发了一个nomogram，可以预测营养不良HCC患者在接受联合治疗后的1、3、5年RFS；所构建的模态图表现出良好的预测能力。

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

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.