Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Mar 7:11:509-523.
doi: 10.2147/JHC.S450825. eCollection 2024.

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Wenying Qiao #  1   2   3   4   5 , Shugui Sheng #  1   2   3 , Junnan Li #  1   2   3 , Ronghua Jin  1   2   3   4 , Caixia Hu  5
Affiliations

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Wenying Qiao et al. J Hepatocell Carcinoma. .

Abstract

Purpose: The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS).

Patients and methods: A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell's concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes.

Results: Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification.

Conclusion: Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

Keywords: HCC; LASSO regression; OS; RSF; hepatocellular carcinoma; nomogram; overall survival; random survival forest.

PubMed Disclaimer

Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Screening independent risk factors for OS using LASSO regression analysis.
Figure 2
Figure 2
Screening independent risk factors for OS using RSF model.
Figure 3
Figure 3
Forest plot display of multivariate Cox regression analysis results.
Figure 4
Figure 4
Nomogram developed for predicting 3-, 5-, and 8-year OS of elderly cirrhotic HCC patients undergoing ablation therapy.
Figure 5
Figure 5
ROC curve analysis of the nomogram for 3-, 5-, and 8-year OS prediction in the training cohort.
Figure 6
Figure 6
Calibration plots of the nomogram for 3-year (A), 5-year (B), and 8-year (C) OS prediction in the training cohort.
Figure 7
Figure 7
DCA curve analysis of the nomogram for 3-year (A), 5-year (B), and 8-year (C) OS prediction in the training cohort.
Figure 8
Figure 8
Kaplan-Meier survival curves for OS based on the nomogram-derived low, intermediate, and high-risk groups in the training cohort.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. doi:10.3322/caac.21660 - DOI - PubMed
    1. Loy LM, Low HM, Choi JY, Rhee H, Wong CF, Tan CH. Variant hepatocellular carcinoma subtypes according to the 2019 WHO classification: an imaging-focused review. AJR Am J Roentgenol. 2022;219(2):212–223. doi:10.2214/ajr.21.26982 - DOI - PubMed
    1. Arora SP, Liposits G, Caird S, et al. Hepatocellular carcinoma in older adults: a comprehensive review by young international society of geriatric oncology. J Geriatr Oncol. 2020;11(4):557–565. doi:10.1016/j.jgo.2019.10.007 - DOI - PMC - PubMed
    1. Abu-Freha N, Estis-Deaton A, Aasla M, et al. Liver cirrhosis in elderly patients: clinical characteristics, complications, and survival-analyses from a large retrospective study. Aging Clin Exp Res. 2022;34(9):2217–2223. doi:10.1007/s40520-022-02152-6 - DOI - PubMed
    1. Minami Y, Aoki T, Hagiwara S, Kudo M. Tips for preparing and practicing thermal ablation therapy of hepatocellular carcinoma. Cancers. 2023;15:19. - PMC - PubMed