Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

328
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...
328
Actuarial Approach01:20

Actuarial Approach

61
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
61
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

96
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.
96
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

94
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,...
94
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

180
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...
180
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

149
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...
149

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Impact of chronic comorbidities on psychological and social status in COVID-19 patients.

Frontiers in psychology·2026
Same author

Resources and applications of public biomedical data.

Frontiers in bioinformatics·2026
Same author

A study on the antitumor effect and mechanism of arsenic trioxide on lung adenocarcinoma.

Journal of cancer research and therapeutics·2026
Same author

Somatic Mutation Trajectories Define Prognostically Distinct Subtypes and Shape the Tumor Microenvironment in Gastric Cancer.

Genes·2026
Same author

Decoding IGLL5 Mutation-Mediated BCR Signaling: A Novel Mechanism of CD8<sup>+</sup> T Cell Exhaustion and Ocular MALT Lymphoma Progression.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Association of transitions in frailty with dementia risk: findings from two longitudinal cohort studies.

Frontiers in medicine·2026

Related Experiment Video

Updated: Jun 4, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

213

Time-dependent interpretable survival prediction model for second primary NSCLC patients.

Qiong Luo1, Qianyuan Zhang2, Haiyu Liu3

  • 1Department of Oncology Medicine, Fujian Medical University Union Hospital, Fuzhou, 350001, PR China.

International Journal of Medical Informatics
|December 25, 2024
PubMed
Summary

This study developed an accurate Blackboost survival model for predicting outcomes in patients with second primary non-small cell lung cancer (SP-NSCLC). The model, utilizing machine learning, identified key predictors like surgery and stage to improve personalized survival predictions.

Keywords:
Machine learningOverall survival predictionSecond primary non-small cell lung cancerSurgeryTime-dependent interpretability

More Related Videos

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

6.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Related Experiment Videos

Last Updated: Jun 4, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

213
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

6.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Area of Science:

  • Oncology
  • Machine Learning in Medicine
  • Survival Analysis

Background:

  • Accurate prediction of overall survival (OS) for second primary non-small cell lung cancer (SP-NSCLC) remains challenging.
  • Existing predictive models for SP-NSCLC have limitations in accuracy and interpretability.

Purpose of the Study:

  • To develop and validate interpretable, time-dependent survival machine learning models for SP-NSCLC patient OS prediction.
  • To identify key predictors influencing OS in SP-NSCLC patients over time.

Main Methods:

  • Utilized SEER database (1988-2020) for SP-NSCLC patients aged 20-89.
  • Developed and validated multiple survival machine learning algorithms (e.g., Blackboost) using C-index, time-AUC, and time-Brier Score.
  • Employed time-dependent interpretability analysis to determine feature importance.

Main Results:

  • The Blackboost model showed excellent performance (C-index: 0.7517, time-AUC: 0.8438) and good calibration.
  • External validation confirmed model robustness, generalizability, and fairness.
  • Surgery was the most critical predictor; combined stage and chemotherapy were important within 5 years, with age becoming significant later.

Conclusions:

  • The Blackboost model provides accurate, fair, and robust OS predictions for SP-NSCLC.
  • Predictor importance varies across survival timelines, with surgery, stage, chemotherapy, and age playing distinct roles.
  • An online visualization tool aids personalized survival prediction for SP-NSCLC patients.