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

Tumor Progression02:07

Tumor Progression

7.2K
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...
7.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

612
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...
612
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

14.5K
Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
14.5K
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

5.8K
Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
5.8K
Cancer02:18

Cancer

53.3K
Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
53.3K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

686
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
686

You might also read

Related Articles

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

Sort by
Same author

Age distinguishes selection from causation in cancer genomes.

Nature genetics·2026
Same author

Mathematical Modeling Quantifies "Just-Right" APC Inactivation for Colorectal Cancer Initiation.

Cancer research·2025
Same author

A mathematical model of ctDNA shedding predicts tumor detection size.

Science advances·2020
Same author

Competing evolutionary paths in growing populations with applications to multidrug resistance.

PLoS computational biology·2019

Related Experiment Video

Updated: Jan 3, 2026

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

606

Cancer recurrence times from a branching process model.

Stefano Avanzini1, Tibor Antal1

  • 1School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom.

Plos Computational Biology
|November 22, 2019
PubMed
Summary

This study models cancer metastasis, finding that early tumor removal is crucial. Delays in surgery significantly increase the risk of detectable metastases and cancer recurrence.

Area of Science:

  • Oncology
  • Mathematical Modeling
  • Cancer Metastasis Research

Background:

  • Metastasis, the spread of cancer cells from a primary tumor, is the leading cause of cancer-related mortality.
  • Understanding the dynamics of metastasis formation and detection is critical for improving patient outcomes.

Purpose of the Study:

  • To develop and analyze a mathematical model of metastasis formation based on primary tumor size.
  • To investigate the earliest time of detectable metastasis after primary tumor resection.
  • To compare model predictions with clinical data for various cancer types.

Main Methods:

  • A conceptually simple model of metastasis initiation, dependent on primary tumor size.
  • Modeling metastasis evolution as independent branching processes.

More Related Videos

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

502
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

10.7K

Related Experiment Videos

Last Updated: Jan 3, 2026

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

606
Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

502
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

10.7K
  • Parameter estimation for breast, colorectal, head and neck, lung, and prostate cancers.
  • Comparison of model predictions with clinical literature data.

    • Main Results:

    • Identified a wide range of primary tumor resection sizes where metastases are likely present but undetectable for certain cancers.
    • Model predicts that only very early resections can effectively prevent cancer recurrence.
    • Demonstrated that even small delays in surgical timing can substantially elevate the probability of recurrence.

    Conclusions:

    • Early surgical intervention is paramount for preventing cancer recurrence.
    • The timing of primary tumor resection significantly influences the likelihood of detectable metastases.
    • The developed model provides valuable insights into metastasis dynamics and recurrence risk across different cancer types.