Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach.
{"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}
引用次数: 0
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.