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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

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The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

188
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Construction Of An Interpretable Model For Predicting Survival Outcomes In Patients With Middle To Advanced Hepatocellular Carcinoma (≥5 Cm) Using Lasso-cox Regression.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Construction Of An Interpretable Model For Predicting Survival Outcomes In Patients With Middle To Advanced Hepatocellular Carcinoma (≥5 Cm) Using Lasso-cox Regression.

Related Experiment Video

An R-Based Landscape Validation of a Competing Risk Model
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Construction of an interpretable model for predicting survival outcomes in patients with middle to advanced hepatocellular carcinoma (≥5 cm) using lasso-cox regression.

Han Li1, Bo Yang1, Chenjie Wang1

  • 1Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Frontiers in Pharmacology
|October 7, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
LASSO-COXhepatocellular carcinomainterpretablenomogram

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This study identifies key risk factors for advanced hepatocellular carcinoma (HCC) larger than 5 cm. A Lasso-Cox model effectively predicts patient outcomes and stratifies risk for better clinical decisions.

Area of Science:

  • Hepatocellular Carcinoma (HCC) research
  • Oncology
  • Medical Informatics

Background:

  • Hepatocellular carcinoma (HCC) with a tumor diameter ≥ 5 cm represents advanced disease.
  • Effective risk stratification is crucial for clinical decision-making in these patients.

Purpose of the Study:

  • To identify key risk factors for HCC (≥ 5 cm diameter).
  • To establish an interpretable predictive model for risk stratification.
  • To utilize Lasso regression for effective clinical decision-making.

Main Methods:

  • Retrospective study including 843 patients with advanced HCC (≥ 5 cm).
  • Lasso regression was used for variable screening, followed by Cox proportional hazard regression and random survival forest (RSF) models.
  • Model performance was compared using Area Under the Curve (AUC), and interpretability was assessed using SHAP values.
radiotherapy

Main Results:

  • Lasso regression identified 8 characteristic risk factors.
  • The Lasso-Cox model demonstrated superior predictive performance (AUCs 0.773-0.799) compared to the Lasso-RSF model (AUCs 0.734-0.741).
  • Key risk factors identified include tumor number, BCLC stage, alkaline phosphatase (ALP), ascites, albumin (ALB), and aspartate aminotransferase (AST).
  • Risk stratification showed significantly better overall survival (OS) in the low-risk group compared to middle- and high-risk groups.

Conclusions:

  • An interpretable predictive model for advanced HCC (≥ 5 cm) was developed using Lasso-Cox regression.
  • The model exhibits excellent prediction performance for risk stratification.
  • This tool can aid clinicians in managing patients with middle and late-stage HCC.