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
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

152
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...
152
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

100
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,...
100
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

97
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
97
  1. Home
  2. Validation Of Two Predictive Models For Survival In Anaplastic Thyroid Cancer (atc).
  1. Home
  2. Validation Of Two Predictive Models For Survival In Anaplastic Thyroid Cancer (atc).

Related Experiment Video

Spontaneous Murine Model of Anaplastic Thyroid Cancer
05:39

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Published on: February 3, 2023

1.6K

Validation of two predictive models for survival in anaplastic thyroid cancer (ATC).

Lukas Käsmann1,2, Alexander Nieto3, Robert Rennollet4

  • 1Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany. Lukas.Kaesmann@med.uni-muenchen.de.

BMC Cancer
|November 29, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The Sugitani Prognostic Index (SPI) accurately predicts survival in anaplastic thyroid cancer (ATC) patients, outperforming the Marchand-Crety Prognostic Score (MCPS). SPI aids in treatment allocation for better patient outcomes.

Keywords:
Anaplastic thyroid cancerMultimodal treatmentPredictionScoreSurvival

More Related Videos

Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma
06:08

Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma

Published on: June 2, 2023

1.7K
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

Related Experiment Videos

Spontaneous Murine Model of Anaplastic Thyroid Cancer
05:39

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Published on: February 3, 2023

1.6K
Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma
06:08

Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma

Published on: June 2, 2023

1.7K
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

Area of Science:

  • Oncology
  • Thyroid Cancer Research
  • Prognostic Biomarkers

Background:

  • Anaplastic thyroid cancer (ATC) has a poor prognosis, necessitating tools to identify long-term survivors for tailored treatment.
  • Accurate prognostic scores are crucial for informed clinical decision-making and patient counseling.
  • This study aimed to validate and compare two prognostic scores in an independent ATC cohort.

Purpose of the Study:

  • To validate the Sugitani Prognostic Index (SPI) and the Marchand-Crety Prognostic Score (MCPS) for predicting survival in anaplastic thyroid cancer.
  • To determine which prognostic index demonstrates superior discrimination of patient survival.
  • To assess the utility of these scores in guiding treatment allocation and understanding patient prognosis.

Main Methods:

  • A cohort of 34 patients with histologically confirmed ATC was retrospectively analyzed.
  • Prognostic discrimination was compared using concordance statistics, AUC, NRI, and IDI for 6-month survival.
  • Next-generation sequencing was performed on a subset of patients, revealing no druggable mutations.
  • Main Results:

    • The Sugitani Prognostic Index (SPI) showed a significantly higher Area Under the Curve (AUC) of 0.85 for predicting 6-month survival compared to MCPS (AUC = 0.69).
    • SPI correctly reclassified 73% of patients for 6-month survival prediction, significantly outperforming MCPS (p=0.0237).
    • Median survival for the cohort was 5 months.

    Conclusions:

    • The Sugitani Prognostic Index (SPI) is a more accurate tool than the Marchand-Crety Prognostic Score (MCPS) for predicting life expectancy in ATC patients.
    • SPI is recommended for clinical guidance and treatment allocation in anaplastic thyroid cancer.
    • Comprehensive genetic profiling remains vital for guiding targeted therapies in ATC.