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
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
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
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
Tumor Progression02:07

Tumor Progression

6.2K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Managing Cancer Pain in Hospitalized Patients with Comorbid Opioid Use Disorder with Buprenorphine: A Case Series.

Journal of palliative medicine·2024
Same author

Assessing the prognostic features of a pain classification system in advanced cancer patients.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2017
Same author

Snapshot of an Outpatient Supportive Care Center at a Comprehensive Cancer Center.

Journal of palliative medicine·2017
Same author

End-of-Life Care Matters: Palliative Cancer Care Results in Better Care and Lower Costs.

The oncologist·2017
Same author

Testing the feasibility of using the Edmonton Symptom Assessment System (ESAS) to assess caregiver symptom burden.

Palliative & supportive care·2017
Same author

Factors associated with patient-reported subjective well-being among advanced lung or non-colonic gastrointestinal cancer patients.

Palliative & supportive care·2017

Related Experiment Video

Updated: Jun 9, 2025

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

Case report: Poor prognosis or poor prognostication?

Jacqueline Tschanz1, Rida Khan1, Eduardo Bruera1

  • 1Department of Palliative Care, Rehabilitation, and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, US.

Palliative & Supportive Care
|October 31, 2024
PubMed
Summary

Accurate survival prognostication in metastatic melanoma is challenging. This case shows a patient improved despite poor predictors, highlighting the need for better communication and tools for patients and families.

Keywords:
Prognosticationadvanced cancerend of lifepalliative careprognosis

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
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

977

Related Experiment Videos

Last Updated: Jun 9, 2025

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
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
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

977

Area of Science:

  • Oncology
  • Palliative Care
  • Medical Ethics

Background:

  • Accurate prognostication in advanced cancer is crucial for patient care and decision-making.
  • Current methods for predicting survival in metastatic melanoma have limitations.
  • Effective communication regarding prognosis is vital for patients and their families.

Purpose of the Study:

  • To highlight the challenges in prognostication for metastatic melanoma.
  • To underscore the impact of inaccurate survival predictions on patients and families.
  • To emphasize the need for improved prognostic tools and communication strategies.

Main Methods:

  • A case report of a 50-year-old patient with metastatic melanoma.
  • Inclusion of intensive care unit (ICU) admission and transfer to a palliative care unit.
  • Subsequent transfer back to the oncology team following clinical improvement.

Main Results:

  • The patient demonstrated clinical improvement despite indicators predicting a poor outcome.
  • The case illustrates a discrepancy between prognostic signs and actual patient trajectory.
  • This unexpected course necessitated reassessment and adjustment of care plans.

Conclusions:

  • Physician overestimation or underestimation of survival time can cause significant distress.
  • There is a critical need for enhanced accuracy in prognostic tools for cancer patients.
  • Further research is essential to improve survival prediction for better patient and family support.