Jove
Visualize
Contact Us

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

429
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...
429
Survival Tree01:19

Survival Tree

138
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...
138

You might also read

Related Articles

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

Sort by
Same author

Editorial: Innovative AI approaches in quantitative MRI: from image enhancement to biomarker estimation.

Frontiers in radiology·2026
Same author

Multimodal Evaluation of Mental Workload and Engagement in Upper-Limb Robot-Assisted Motor Tasks.

Sensors (Basel, Switzerland)·2026
Same author

Impacts of an antioxidant-rich diet and lifestyle factors on gut microbiota diversity and brain health: An exploratory analysis from the NutBrain study.

Clinical nutrition (Edinburgh, Scotland)·2026
Same author

A Comprehensive Framework for Uncertainty Quantification of Voxel-Wise Supervised Deep Learning Models in IVIM MRI.

NMR in biomedicine·2026
Same author

Segmentation Variability in Bayesian U-Net versus Manual Annotations: Impact on Radiomic Reproducibility in Lung Tumor CT Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.

Journal of the American Society of Nephrology : JASN·2025
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 Experiment Video

Updated: Aug 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Automatic Feature Construction Based on Genetic Programming for Survival Prediction in Lung Cancer Using CT Images.

Elisa Scalco, Giovanna Rizzo, Wilfrido Gomez-Flores

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    Genetic Programming (GP) effectively reduces radiomic features for Non-Small Cell Lung Cancer (NSCLC) classification. This approach improves prediction of two-year survival from CT scans, outperforming traditional methods.

    More Related Videos

    Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
    03:39

    Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

    Published on: June 20, 2025

    274
    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

    6.9K

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.3K
    Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
    03:39

    Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

    Published on: June 20, 2025

    274
    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

    6.9K

    Area of Science:

    • Radiomics
    • Machine Learning
    • Medical Imaging Analysis

    Background:

    • Radiomics analysis uses machine learning for classification models.
    • Irrelevant and redundant features can decrease classification performance.
    • Effective feature selection is crucial for accurate medical predictions.

    Purpose of the Study:

    • To propose Genetic Programming (GP) for automatic construction of reduced, relevant radiomic features.
    • To enhance classification performance in Non-Small Cell Lung Cancer (NSCLC) prediction.
    • To improve patient stratification based on overall postoperative survival.

    Main Methods:

    • Application of the Genetic Programming (GP) algorithm to select independent and relevant radiomic features.
    • Utilizing pre-operative computed tomography (CT) images from NSCLC patients.
    • Developing linear classifiers to predict two-year survival.

    Main Results:

    • The GP-based model demonstrated superior classification performance ([Formula: see text]) compared to benchmark models ([Formula: see text] and 0.64).
    • The proposed method successfully reduced the number of radiomic features while maintaining relevance.
    • Improved accuracy in stratifying patients by high and low risk of mortality.

    Conclusions:

    • Genetic Programming offers a powerful tool for optimizing radiomic feature selection.
    • The GP-based approach enhances the predictive accuracy of survival in NSCLC patients.
    • This method holds promise for personalized risk stratification in cancer care.