Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach.

IF 4.2 3区 医学 Q2 ONCOLOGY
Journal of Hepatocellular Carcinoma Pub Date : 2025-04-14 eCollection Date: 2025-01-01 DOI:10.2147/JHC.S512410
Chiyu Cai, Hengli Zhu, Bingyao Li, Changqian Tang, Yongnian Ren, Yuqi Guo, Jizhen Li, Liancai Wang, Deyu Li, Dongxiao Li
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Abstract

Purpose: As the global population ages, precise prognostic tools are needed to optimize postoperative care for elderly hepatocellular carcinoma (HCC) patients. This study established a machine learning-driven predictive model to identify key prognostic determinants and evaluate age/surgical approach impacts, overcoming limitations of traditional statistical methods.

Methods: This retrospective study included 252 postoperative HCC patients aged ≥65 years (mean age 69.0±4.3; 68.25% male). Patients were randomly divided into training (70%, n=177) and validation sets (30%, n=75). We evaluated 147 machine learning models to establish the optimal predictive model. Patients were grouped by age (>75 vs ≤75 years) and surgical approach (laparoscopic vs open).

Results: The LASSO+RSF model showed strong predictive performance with AUC values of 0.869 and 0.818 in the training and validation sets, respectively. Time-dependent AUCs for 1-, 2- and 3-year survival were 0.874, 0.903, and 0.883 in the training set, and 0.878, 0.882, and 0.915 in the validation set. Key predictors included age-adjusted Charlson index (ACCI, LASSO+RSF synergistic weight (LRSW) =0.160), microvascular invasion (0.111), tumor capsule integrity (0.034), and lymphatic invasion (0.023), while three variables (intraoperative blood loss, tumor margin, WBC) were excluded (LRSW<0.01). A web-based dynamic nomogram (https://cliniometrics.shinyapps.io/LRSF-GeroHCC/) enabled real-time risk stratification. Patients >75 years had longer length of stay (16 vs 14 days, P=0.033), higher Clavien-Dindo scores (P=0.014), higher ACCI scores (5.5 vs 4.0, P=0.002), and lower PFS (16.5 vs 24 months, P=0.041). Laparoscopic surgery was associated with longer operative time (202.5 vs 159.0min, P<0.001), shorter length of stay (14 vs 17days, P<0.001), and lower Clavien-Dindo scores (P=0.038).

Conclusion: The LASSO+RSF model provides validated tools for personalized prognosis management in elderly HCC patients, emphasizing age-adapted surgical strategies and comorbidity-focused perioperative care.

老年肝癌患者的预后分析:基于年龄分层和手术方法的探索和机器学习模型预测。
目的:随着全球人口老龄化,需要精确的预后工具来优化老年肝细胞癌(HCC)患者的术后护理。本研究建立了一个机器学习驱动的预测模型,以确定关键的预后决定因素并评估年龄/手术方式的影响,克服了传统统计方法的局限性。方法:回顾性研究纳入252例术后HCC患者,年龄≥65岁(平均年龄69.0±4.3;68.25%的男性)。患者随机分为训练组(70%,n=177)和验证组(30%,n=75)。我们评估了147个机器学习模型,以建立最优的预测模型。患者按年龄(75岁以下vs≤75岁)和手术方式(腹腔镜vs开放)分组。结果:LASSO+RSF模型在训练集和验证集的AUC值分别为0.869和0.818,具有较强的预测性能。1年、2年和3年生存率的时间相关auc在训练集中分别为0.874、0.903和0.883,在验证集中分别为0.878、0.882和0.915。关键预测因子包括年龄调整Charlson指数(ACCI, LASSO+RSF增力权重(LRSW) =0.160)、微血管侵袭(0.111)、肿瘤包膜完整性(0.034)和淋巴侵袭(0.023),而排除术中出血量、肿瘤边缘、WBC三个变量(LRSW75岁患者住院时间较长(16天vs 14天,P=0.033)、Clavien-Dindo评分较高(P=0.014)、ACCI评分较高(5.5比4.0,P=0.002)、PFS较低(16.5比24个月,P=0.041)。腹腔镜手术的手术时间较长(202.5 vs 159.0min, PPP=0.038)。结论:LASSO+RSF模型为老年HCC患者的个性化预后管理提供了行之有效的工具,强调了适合年龄的手术策略和以合并症为重点的围手术期护理。
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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