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

345
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...
345
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

5.5K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

183
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...
183
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

136
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,...
136
Tumor Progression02:07

Tumor Progression

6.3K
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...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Genomic Classification to Predict Survival in Metastatic Prostate Cancer: Development of Somatic Tumor Risk Assessment for Overall Survival-Prostate.

JCO precision oncology·2026
Same author

Can ovarian cancer screening work? A secondary analysis of the UK collaborative trial of ovarian cancer screening.

British journal of cancer·2026
Same author

Colorectal cancer screening: An update to the American Cancer Society guideline, 2026.

CA: a cancer journal for clinicians·2026
Same author

Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer.

Molecular cancer research : MCR·2026
Same author

Impact of Multicancer Screening on Late-Stage Cancer at Diagnosis in Breast Cancer Survivors: A Modeling Study.

JCO precision oncology·2026
Same author

PSMA PET/CT-derived Tumor Volume for Predicting Outcomes in Patients with Metastatic Castration-Resistant Prostate Cancer Receiving <sup>177</sup>Lu-PSMA-617.

Radiology·2026

Related Experiment Video

Updated: Jun 30, 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

263

Projecting the Impact of Multi-Cancer Early Detection on Late-Stage Incidence Using Multi-State Disease Modeling.

Jane M Lange1, Kemal Caglar Gogebakan2, Roman Gulati2

  • 1Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, Oregon.

Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology
|March 20, 2024
PubMed
Summary

Multi-cancer early detection (MCED) tests can reduce late-stage cancer incidence. Even short-term trials show significant downstaging potential with adequate early-stage test sensitivity.

More Related Videos

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

5.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jun 30, 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

263
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

5.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Oncology
  • Biostatistics
  • Medical Informatics

Background:

  • Downstaging, a reduction in late-stage cancer incidence, is a proposed endpoint for multi-cancer early detection (MCED) trials.
  • Current understanding of downstaging is limited for cancers lacking existing screening and for MCED tests covering these types.

Purpose of the Study:

  • To develop and apply a cancer natural history model to project downstaging in MCED trials.
  • To assess the impact of test performance and preclinical latency on downstaging for cancers without existing screening.

Main Methods:

  • A cancer natural history model was developed, fitted to Surveillance, Epidemiology, and End Results (SEER) registry incidence data for 12 cancers.
  • The model was used to project downstaging in simulated MCED trials, considering variable preclinical latencies and stage-specific test sensitivities.

Main Results:

  • Modeled downstaging ranged from 21% to 43% across plausible preclinical latencies in a hypothetical 3-screen MCED trial.
  • Reductions in late-stage incidence were observed soon after screening initiation.
  • Downstaging efficacy increased with longer early-stage latency and higher early-stage test sensitivity.

Conclusions:

  • Short-term MCED trials can achieve substantial downstaging if early-stage test sensitivity is adequate.
  • This modeling framework supports the analysis of novel MCED products and trial designs, particularly those using late-stage incidence as a primary endpoint.