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

863
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...
863
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

712
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...
712
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

792
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,...
792
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

955
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...
955
Actuarial Approach01:20

Actuarial Approach

384
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,...
384
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Plant-derived mitochondria mitigate aging-related neurodegeneration by reprogramming microglial mitochondrial energy metabolism.

Translational neurodegeneration·2026
Same author

Metabolic syndrome-associated gut microbiota and plasma metabolite profiles in schizophrenia.

Translational psychiatry·2026
Same author

DLP bioprinting of cartilage organoid-laden bioinks yields high-fidelity auricular constructs with enhanced chondrogenesis.

Stem cell research & therapy·2026
Same author

An Oxazine-Locked Covalent Organic Framework by a Tandem Pinner/Schiff Base Reaction for Hydrogen Peroxide Photosynthesis.

Journal of the American Chemical Society·2026
Same author

Key molecular mechanisms of mitochondrial metabolic pathways in specific cell subpopulations of pancreatic cancer based on scRNA-seq and bulk RNA-seq.

PloS one·2026
Same author

Application of Cerium-Tannic Acid-Formaldehyde Coordination Polymer Colloidal Nanomaterials to Alleviate Lipopolysaccharide-Induced Acute Lung Injury.

International journal of nanomedicine·2026

Related Experiment Video

Updated: May 3, 2026

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

1.0K

An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.

Shuchao Chen1, Haojiang Li2, Hui Mao3

  • 1School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, 541004, China.

Medical & Biological Engineering & Computing
|May 1, 2026
PubMed
Summary

SurvS, a survival supervision framework, improves cancer treatment decisions by directly analyzing survival benefits. This interpretable model identifies beneficial, insensitive, or detrimental outcomes for personalized treatment planning.

Keywords:
CancerIndividual treatment effectReal-coded genetic algorithmSurvival benefit analysisTreatment decision-making

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.6K
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

9.1K

Related Experiment Videos

Last Updated: May 3, 2026

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

1.0K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.6K
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

9.1K

Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Optimal cancer treatment decisions are crucial for patient survival.
  • Traditional models indirectly assess risks, limiting interpretability and performance.
  • Need for interpretable frameworks to personalize cancer treatment.

Purpose of the Study:

  • Introduce SurvS (survival supervision), a novel interpretable framework for survival benefit analytics.
  • Integrate individual treatment effects directly impacting long-term survival.
  • Develop both binary and ternary decision-making models for personalized cancer treatment.

Main Methods:

  • Utilized a real-coded genetic algorithm for survival benefit analysis.
  • Developed SurvS to construct personalized decision-making models.
  • Integrated weighted clinical features and real-valued cutoff thresholds for model optimization.

Main Results:

  • Demonstrated robust performance in nasopharyngeal and rectal cancer treatment scenarios.
  • SurvS outperformed traditional decision-making methods.
  • Maintained strong performance in an independent validation cohort.

Conclusions:

  • SurvS provides a powerful tool for personalized cancer treatment planning.
  • Enables survival benefit-supervised optimization and interpretable model construction.
  • Potential to improve treatment efficacy and reduce overtreatment in oncology.