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

522
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...
522
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.6K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.6K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

263
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
263
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.4K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Targeted degradation of MDM2 overcomes feedback regulation of p53 signaling in Merkel cell carcinoma models.

The Journal of clinical investigation·2026
Same author

ESR1 mutations and CDK4/6 inhibitor choice shape clonal selection and adaptive cell states during acquired resistance.

Genome medicine·2026
Same author

A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution.

Cancer discovery·2026
Same author

H3K27M-driven hypertranscription leads to a new targetable dependency in diffuse midline gliomas.

bioRxiv : the preprint server for biology·2026
Same author

Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

HER2 heterogeneous breast cancer models reveal novel therapeutic targets and subclonal dynamics during evolution to resistance to HER2-targeted therapies.

Cancer discovery·2026

Related Experiment Video

Updated: Nov 16, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

12.1K

A Quantitative Paradigm for Decision-Making in Precision Oncology.

Dalit Engelhardt1, Franziska Michor2

  • 1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA.

Trends in Cancer
|February 27, 2021
PubMed
Summary

Developing dynamic, personalized cancer treatment strategies requires quantitative methods. Integrating longitudinal data with mathematical modeling and machine learning, especially reinforcement learning, is key to optimizing therapy under uncertainty.

Keywords:
machine learningmathematical modelingpersonalized medicinetreatment optimization

More Related Videos

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.2K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.3K

Related Experiment Videos

Last Updated: Nov 16, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

12.1K
Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.2K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.3K

Area of Science:

  • Oncology
  • Computational Biology
  • Data Science

Background:

  • Cancer progression is complex and variable, demanding adaptive treatment plans.
  • Current therapeutic decision-making often lacks dynamic and personalized quantitative approaches.
  • Significant uncertainty exists in predicting individual patient responses to treatment.

Purpose of the Study:

  • To outline the essential components and challenges of a quantitative paradigm for dynamic cancer treatment.
  • To emphasize the necessity of integrating comprehensive longitudinal clinical and molecular data.
  • To explore the roles of mathematical modeling and machine learning in developing personalized cancer therapies.

Main Methods:

  • Discussed core components and challenges of quantitative therapeutic decision-making.
  • Highlighted the need for longitudinal data integration.
  • Described the application of mathematical modeling and machine learning (ML).
  • Emphasized reinforcement learning (RL) for dynamic strategy construction.

Main Results:

  • Identified key requirements for a quantitative, dynamic, and personalized cancer treatment framework.
  • Demonstrated the complementary roles of mathematical modeling and ML in treatment optimization.
  • Showcased the potential of reinforcement learning for adaptive therapeutic strategies.

Conclusions:

  • A quantitative, data-driven approach is essential for personalized cancer therapy.
  • Integration of longitudinal data is critical for building robust treatment models.
  • Mathematical modeling, ML, and RL offer powerful tools for optimizing cancer treatment strategies under uncertainty.