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

478
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...
478
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

296
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,...
296
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.2K
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...
5.2K
Actuarial Approach01:20

Actuarial Approach

146
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,...
146
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

429
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...
429
Survival Tree01:19

Survival Tree

178
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
178

You might also read

Related Articles

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

Sort by
Same author

Tirzepatide attenuates neurotoxicity by suppressing inflammation, apoptosis and restoring neurotrophin expression in an Alzheimer's disease-like rat model.

Metabolic brain disease·2026
Same author

Boosting the Diversity of a Similarity-Aware Genetic Algorithm Using a Siamese Network for Optimized S-Box Generation.

Entropy (Basel, Switzerland)·2026
Same author

PPAR-α Agonist Suppresses Expression of Immune Mediators in B Cells in a Murine Model of Systemic Lupus Erythematosus.

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Bidirectional multi-nodes quantum teleportation using discrete-time quantum walk.

Scientific reports·2025
Same author

Challenges and opportunities in direct-to-consumer hearing healthcare service delivery: a scoping review.

Disability and rehabilitation. Assistive technology·2025
Same author

Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Oct 7, 2025

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

9.1K

An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques.

Ishleen Kaur1, M N Doja1, Tanvir Ahmad1

  • 1Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India.

Computational Intelligence and Neuroscience
|January 7, 2022
PubMed
Summary
This summary is machine-generated.

Predicting ovarian cancer survival is crucial for patient care. Data mining reveals that treatment sequences and life quality significantly impact survival outcomes in advanced ovarian cancer patients.

More Related Videos

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

414
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Related Experiment Videos

Last Updated: Oct 7, 2025

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

9.1K
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

414
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Area of Science:

  • Oncology
  • Data Science
  • Medical Informatics

Background:

  • Ovarian cancer is a leading gynecologic cancer with high mortality in advanced stages.
  • Accurate survival prediction is vital for clinical management and patient understanding.
  • Identifying key prognostic factors remains a challenge in advanced ovarian cancer.

Purpose of the Study:

  • To investigate survival predictors for advanced ovarian cancer prognosis.
  • To apply data mining techniques for enhanced cancer survival estimation.
  • To integrate diverse patient data profiles for improved predictive accuracy.

Main Methods:

  • Collected and preprocessed data from 140 advanced ovarian cancer patients (clinical, treatment, life quality).
  • Utilized sequence mining for treatment pattern analysis and Charlson Comorbidity Index for comorbidity assessment.
  • Employed machine learning algorithms, including ensemble techniques, for survival prediction.

Main Results:

  • An integrated model achieved a 76.4% accuracy in predicting patient survival.
  • Ensemble methods combined with sequential pattern mining (2-month intervals) yielded the highest accuracy.
  • Treatment sequences and life quality attributes were identified as significant contributors to survival prediction.

Conclusions:

  • Treatment sequences and life quality are critical factors in advanced ovarian cancer survival prediction.
  • Data mining and machine learning offer powerful tools for enhancing cancer prognosis.
  • Integrated patient data improves the accuracy of survival estimation in oncology.