Jove
Visualize
Contact Us

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

329
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...
329
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

156
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...
156
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

104
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,...
104
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
  1. Home
  3. Screening Of Prognostic Factors And Survival Analysis Based On Histological Type For Perimenopausal Endometrial Carcinoma Treated With Hysterectomy.
  1. Home
  3. Screening Of Prognostic Factors And Survival Analysis Based On Histological Type For Perimenopausal Endometrial Carcinoma Treated With Hysterectomy.

Related Experiment Video

An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells
09:48

An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells

Published on: September 12, 2019

8.0K

Screening of prognostic factors and survival analysis based on histological type for perimenopausal endometrial

Luyao Kang1, Gaili Ji1, Duan Liu1

  • 1Department of Gynecologic Oncology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.

Discover Oncology
|October 2, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study analyzed prognostic factors for perimenopausal endometrial carcinoma (PIPEC) patients. Histological type significantly impacts survival, with low-grade endometrioid carcinoma showing the best prognosis.

Keywords:
Endometrial carcinomaPerimenopausePrognosisRegional lymph nodes statusSEER

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

223
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Related Experiment Videos

An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells
09:48

An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells

Published on: September 12, 2019

8.0K
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

223
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Area of Science:

  • Gynecologic Oncology
  • Cancer Epidemiology
  • Survival Analysis

Background:

  • Endometrial carcinoma in perimenopausal women presents unique challenges.
  • Understanding prognostic factors is crucial for tailoring treatment strategies.

Purpose of the Study:

  • To explore prognostic factors for perimenopausal endometrial carcinoma (PIPEC) patients.
  • To analyze survival patterns based on histological type after hysterectomy.

Main Methods:

  • Utilized the Surveillance, Epidemiology, and End Results (SEER) database.
  • Employed random survival forest and Cox regression for prognostic factor identification.
  • Analyzed overall survival (OS) and cancer-specific survival (CSS) by histological type, lymph node status, and SEER stage.

Main Results:

  • Identified tumor size, grade, histology, SEER stage, AJCC stage, metastasis, and lymph node status as significant prognostic factors.
  • Low-grade endometrioid carcinoma demonstrated the best prognosis, while carcinosarcoma and undifferentiated carcinoma had poorer outcomes.
  • Survival patterns varied significantly among different histological types and changed over time.

Conclusions:

  • Established key prognostic factors for PIPEC patients undergoing hysterectomy.
  • Demonstrated diverse survival patterns across histological types, informing clinical decision-making.
  • Findings aid in guiding personalized treatment approaches for PIPEC.