{"title":"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。","authors":"Chiyu Cai, Hengli Zhu, Bingyao Li, Changqian Tang, Yongnian Ren, Yuqi Guo, Jizhen Li, Liancai Wang, Deyu Li, Dongxiao Li","doi":"10.2147/JHC.S512410","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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, <i>P</i>=0.033), higher Clavien-Dindo scores (<i>P</i>=0.014), higher ACCI scores (5.5 vs 4.0, <i>P</i>=0.002), and lower PFS (16.5 vs 24 months, <i>P</i>=0.041). Laparoscopic surgery was associated with longer operative time (202.5 vs 159.0min, <i>P</i><0.001), shorter length of stay (14 vs 17days, <i>P</i><0.001), and lower Clavien-Dindo scores (<i>P</i>=0.038).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"747-764"},"PeriodicalIF":4.2000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007611/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach.\",\"authors\":\"Chiyu Cai, Hengli Zhu, Bingyao Li, Changqian Tang, Yongnian Ren, Yuqi Guo, Jizhen Li, Liancai Wang, Deyu Li, Dongxiao Li\",\"doi\":\"10.2147/JHC.S512410\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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, <i>P</i>=0.033), higher Clavien-Dindo scores (<i>P</i>=0.014), higher ACCI scores (5.5 vs 4.0, <i>P</i>=0.002), and lower PFS (16.5 vs 24 months, <i>P</i>=0.041). Laparoscopic surgery was associated with longer operative time (202.5 vs 159.0min, <i>P</i><0.001), shorter length of stay (14 vs 17days, <i>P</i><0.001), and lower Clavien-Dindo scores (<i>P</i>=0.038).</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":15906,\"journal\":{\"name\":\"Journal of Hepatocellular Carcinoma\",\"volume\":\"12 \",\"pages\":\"747-764\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007611/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hepatocellular Carcinoma\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/JHC.S512410\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hepatocellular Carcinoma","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/JHC.S512410","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
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。
Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach.
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.
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