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

You might also read

Related Articles

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

Sort by
Same author

Identifying and Addressing Barriers to Live Primary Prostate Cancer Cell Research in Veterans.

Cancer research communications·2026
Same author

Convergent evolution of complex structural variants drives therapy resistance in metastatic prostate cancer.

Genome biology·2026
Same author

Matching Patients With Cell Surface-Targeted Clinical Trials Using Large Language Models.

JCO precision oncology·2026
Same author

Clinical Utility of Transcriptomic Signatures to Identify Androgen Receptor and Neuroendocrine Signaling in Prostate Cancer.

JCO precision oncology·2026
Same author

Phase I multi-center clinical and biomarker study of the dual-action androgen receptor inhibitor ONCT-534.

Investigational new drugs·2026
Same author

Multi-Institutional Study Evaluating the Role of Early Circulating Tumor DNA Dynamics During Treatment With Immune Checkpoint Inhibitors in Patients With Advanced-Stage Melanoma.

JCO precision oncology·2026

Related Experiment Video

Updated: Jul 26, 2025

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

6.9K

Machine Learning & Molecular Radiation Tumor Biomarkers.

Nicholas R Rydzewski1, Kyle T Helzer2, Matthew Bootsma2

  • 1Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Department of Human Oncology, University of Wisconsin, Madison, WI.

Seminars in Radiation Oncology
|June 18, 2023
PubMed
Summary

Developing radiation tumor biomarkers is key for personalized cancer medicine. Machine learning and "omics" data analysis help predict patient responses to radiotherapy, improving treatment decisions.

More Related Videos

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

7.9K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K

Related Experiment Videos

Last Updated: Jul 26, 2025

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

6.9K
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

7.9K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K

Area of Science:

  • Oncology and Computational Biology
  • Precision Medicine
  • Radiotherapy Research

Background:

  • Personalized cancer medicine requires biomarkers to guide radiotherapy decisions.
  • High-throughput molecular assays and computational methods can identify tumor-specific signatures.
  • Understanding patient outcomes in response to radiotherapy is crucial for treatment optimization.

Purpose of the Study:

  • To review the computational framework for developing radiation tumor biomarkers.
  • To describe machine learning approaches for radiation biomarker discovery using molecular data.
  • To highlight challenges and emerging trends in this field.

Main Methods:

  • Review of computational strategies for biomarker development.
  • Description of machine learning techniques applied to "omics" data.
  • Discussion of analytical strategies for complex, high-throughput data.

Main Results:

  • Machine learning can detect subtle patterns in molecular data for biomarker identification.
  • Careful selection of analytical strategies is needed for complex "omics" data.
  • Ensuring generalizability of machine learning models is a key consideration.

Conclusions:

  • Computational approaches, particularly machine learning, are vital for radiation biomarker development.
  • Advances in molecular profiling and computational biology enable personalized radiotherapy.
  • Addressing data complexity and model generalizability are critical for clinical translation.