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
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

102
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,...
102
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

155
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...
155
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Personalized Three-year Survival Prediction And Prognosis Forecast By Interpretable Machine Learning For Pancreatic Cancer Patients: A Population-based Study And An External Validation.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Personalized Three-year Survival Prediction And Prognosis Forecast By Interpretable Machine Learning For Pancreatic Cancer Patients: A Population-based Study And An External Validation.

Related Experiment Video

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

8.2K

Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation.

Buwei Teng1, Xiaofeng Zhang1, Mingshu Ge1

  • 1Department of Hepatobiliary Surgery, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, China.

Frontiers in Oncology
|November 5, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
SEERmachine learningpancreatic cancerprognosis prediction

More Related Videos

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

6.7K
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

222

Related Experiment Videos

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

8.2K
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

6.7K
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

222

Machine learning models accurately predict three-year survival and prognosis for pancreatic cancer patients. These models offer personalized predictions, improving patient care and outcomes.

Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Pancreatic cancer has a very low overall survival rate.
  • Accurate prediction of survival and prognosis is crucial for patient management.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting three-year survival and prognosis in pancreatic cancer patients.
  • To identify key factors influencing patient survival using ML interpretability techniques.

Main Methods:

  • Analysis of 20,064 pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2021).
  • Feature selection using Recursive Feature Elimination (RFE) with six ML algorithms.
  • Evaluation of 13 ML algorithms for predictive performance using metrics like AUC, accuracy, and sensitivity.
three-year survival
  • Prognostic model development using 101 ML algorithm combinations, assessed by C-index.

    • Main Results:

    • The CatBoost model achieved high predictive accuracy for three-year survival (AUC 0.932 in training, 0.899 in internal test, 0.826 in external test).
    • Surgery type was identified as the most significant factor affecting three-year survival via SHapley Additive exPlanations (SHAP).
    • The "RSF+GBM" algorithm demonstrated the best performance for prognostic prediction (C-index 0.774 in training).

    Conclusions:

    • Developed ML models show excellent accuracy and reliability for predicting pancreatic cancer patient outcomes.
    • These models provide a foundation for more precise, personalized prognostic predictions.
    • The findings can aid in clinical decision-making and patient counseling for pancreatic cancer.