Jove
Visualize
Contact Us

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

334
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...
334
Actuarial Approach01:20

Actuarial Approach

68
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
68
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

162
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...
162
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

111
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,...
111
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. A Nomogram For Overall Survival Of Second Primary Cancers Following Upper-tract Urothelial Carcinoma: A Seer Population-based Study.
  1. Home
  3. A Nomogram For Overall Survival Of Second Primary Cancers Following Upper-tract Urothelial Carcinoma: A Seer Population-based Study.

Related Experiment Video

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

226

A nomogram for overall survival of second primary cancers following upper-tract urothelial carcinoma: a SEER

Xi Zhang1,2, Weikang Chen3, Chunming Li1,2

  • 1Department of Gynecology, Zhejiang University School of Medicine Women's Hospital, Hangzhou, China.

Translational Cancer Research
|September 12, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Second primary malignancies (SPMs) are increasingly identified in upper-tract urothelial carcinoma (UTUC) patients. This study developed a nomogram to predict survival for UTUC patients with SPMs, aiding in risk stratification.

Keywords:
Surveillance, Epidemiology, and End Results (SEER)Upper-tract urothelial carcinoma (UTUC)nomogramprognosissecond primary

More Related Videos

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
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Related Experiment 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

226
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
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Area of Science:

  • Urology
  • Oncology
  • Cancer Epidemiology

Background:

  • Improving survival rates in upper-tract urothelial carcinoma (UTUC) have led to increased identification of second primary malignancies (SPMs).
  • Limited research exists on the incidence and prognostic factors of SPMs in UTUC survivors.
  • Understanding SPM risk is crucial for comprehensive patient management.

Purpose of the Study:

  • To evaluate the incidence and risk of SPMs in patients diagnosed with UTUC.
  • To develop and validate a predictive nomogram for overall survival (OS) in UTUC patients who develop SPMs.

Main Methods:

  • Utilized data from the Surveillance, Epidemiology, and End Results (SEER) database.
  • Assessed SPM prevalence and risk factors using univariate and multivariate Cox regression analyses.
  • Developed and validated an overall survival (OS) nomogram for SPM patients post-UTUC diagnosis.

    • Main Results:

    • The prevalence of SPMs among UTUC patients was 30.23%, with solid tumors being the most common type.
    • The overall risk of SPMs was significantly elevated across all subgroups.
    • The developed nomogram demonstrated good predictive performance for 3- and 5-year OS (C-index: 0.72 training, 0.71 validation).

    Conclusions:

    • This study provides the first comprehensive analysis of SPM incidence following UTUC.
    • A novel nomogram is introduced to predict the prognosis of SPMs in UTUC patients.
    • The findings aid in identifying high-risk individuals and tailoring follow-up strategies.