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

327
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...
327

You might also read

Related Articles

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

Sort by
Same author

The mental health and wellbeing outcomes and mechanisms of change in arts inclusive programs for children aged 6-12: a systematic review.

Frontiers in health services·2026
Same author

Triage safety of patient-facing AI chatbots for nipple discharge: A guideline-informed assessment of red-flag recognition and patient actionability.

International journal of medical informatics·2026
Same author

Study-level factors associated with hematoma after ultrasound-guided vacuum-assisted breast lesion excision: a systematic review and meta-analysis using a T-P-B framework.

Frontiers in oncology·2026
Same author

Reconceptualizing glioblastoma immunotherapy: a four-pillar framework to overcome multidimensional resistance.

Frontiers in medicine·2026
Same author

Impact of Proton Pump Inhibitors on Immune-related Adverse Events and Efficacy During Pembrolizumab Monotherapy.

Anticancer research·2026
Same author

Systematic Evaluation of Signal Peptide-Driven Protein Secretion in the Fast-Growing Cyanobacterium <i>Synechococcus</i> sp. PCC 11901.

Biomolecules·2026

Related Experiment Video

Updated: Jun 1, 2025

Spontaneous Murine Model of Anaplastic Thyroid Cancer
05:39

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Published on: February 3, 2023

1.6K

Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma.

Yihan Sun1, Da Lin2, Xiangyang Deng3

  • 1Department of Thyroid Breast Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.

Discover Oncology
|January 21, 2025
PubMed
Summary

Random survival forests (RSF) provide more accurate survival predictions for anaplastic thyroid carcinoma (ATC) patients than traditional Cox models. This machine learning approach also offers superior prognostic stratification for improved patient outcomes.

Keywords:
Anaplastic thyroid carcinomaPrognosisRandom survival forestsSurvival predictionTraditional Cox model

More Related Videos

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma
07:01

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma

Published on: April 17, 2013

21.0K
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

477

Related Experiment Videos

Last Updated: Jun 1, 2025

Spontaneous Murine Model of Anaplastic Thyroid Cancer
05:39

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Published on: February 3, 2023

1.6K
An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma
07:01

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma

Published on: April 17, 2013

21.0K
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

477

Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Anaplastic thyroid carcinoma (ATC) is an aggressive cancer with limited prognostic tools.
  • Accurate prediction of survival outcomes is crucial for effective patient management.

Purpose of the Study:

  • To develop and validate a prognostic model for ATC patients using random survival forests (RSF).
  • To compare the performance of RSF against traditional Cox models for predicting survival.

Main Methods:

  • Utilized data from 1222 ATC patients from the SEER database.
  • Developed and compared RSF and Cox models using training and validation cohorts.
  • Evaluated model performance using Brier score, C-index, AUC, and Decision Curve Analysis (DCA).

Main Results:

  • The RSF model demonstrated superior performance with a lower Brier score (0.055 vs. 0.063) and higher AUC compared to the Cox model.
  • Surgery, radiotherapy, and chemotherapy were identified as key predictors of survival.
  • RSF successfully stratified ATC patients into two distinct prognostic groups.

Conclusions:

  • Random survival forests (RSF) offer more precise survival predictions for ATC patients.
  • RSF provides superior prognostic stratification compared to Cox regression models for anaplastic thyroid carcinoma.