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A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma

A Study Based on Seer Database

Bibliographic Data

ID22074017
AuthorsTaiyu He (0000-0002-4420-6364, Dalian Medical University), Tianyao Chen (0000-0002-5832-6930, Chongqing Medical University), Xiaozhu Liu (0000-0002-7800-3100, Dalian Medical University), Biqiong Zhang (Dalian Medical University), Song Yue (0000-0003-1338-2549, Dalian Medical University), Junyi Cao (0000-0002-5025-7838, Zigong First People's Hospital), Gaoli Zhang (Dalian Medical University, corresponding author)
Year2022
Volume9
Pages789026-789026
Publication date2022-01-13
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2021.789026
PMID35096742
OpenAlexW4205256181
LanguageEN
Citations received1
References cited32

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking. Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort ( n = 1093) and validation cohort ( n = 461). Patients' data in 2016 ( n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests. Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed. Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients

Cancer · Cancer registry · Cohort · Concordance · Database · Hepatocellular carcinoma · Multivariate analysis · Multivariate statistics · Nomogram · Receiver operating characteristic · Statistics · Surveillance, Epidemiology, and End Results · Univariate · Cancer, Lipids, and Metabolism · Ferroptosis and cancer prognosis · Hepatocellular Carcinoma Treatment and Prognosis · Medicine · Epidemiology · Internal Medicine · Oncology

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Citations per year0.25
Citation span2022 - 2022 (1)
Highly citedNo

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