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
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
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...
4.9K
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

You might also read

Related Articles

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

Sort by
Same author

Sustainable electrochemical sensor utilizing polyaniline-vanadium pentoxide (PANI-V<sub>2</sub>O<sub>5</sub>) nanocomposite for recognition of emamectin benzoate insecticide.

Pest management science·2026
Same author

Clinico-Morphological and Genetic Features of Colorectal Cancer in Multiple Primary Cancers Cases.

Biochemical genetics·2026
Same author

In-Line NMR Diagnostics of Hydroformylation Provided by the Segmented-Flow Microfluidic Regime.

Analytical chemistry·2025
Same author

Contrasting Magnetic Characteristics of Disordered Nd<sub>0.5</sub>Ba<sub>0.5</sub>Mn<sub>0.5</sub>Fe<sub>0.5</sub>O<sub>3-δ/2</sub> and 112-Type Ordered NdBaMnFeO<sub>6-δ</sub> Perovskites.

ACS omega·2024
Same author

Optimal Dynamic Regimes for CO Oxidation Discovered by Reinforcement Learning.

ACS omega·2024
Same author

Application of Artificial Intelligence at All Stages of Bone Tissue Engineering.

Biomedicines·2024

Related Experiment Video

Updated: Jun 29, 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

6.8K

Machine Learning Meets Cancer.

Elena V Varlamova1, Maria A Butakova1, Vlada V Semyonova2

  • 1The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova Str., 344090 Rostov-on-Don, Russia.

Cancers
|March 28, 2024
PubMed
Summary

Machine learning (ML), a part of artificial intelligence (AI), is increasingly vital in oncology for faster diagnosis and treatment planning. ML enhances medical image analysis, prognosis prediction, and drug synthesis, improving patient care despite ethical challenges.

Keywords:
PET/CTartificial intelligencemachine learningoncologyradiomics

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Jun 29, 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

6.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Machine Learning
  • Medical Informatics

Background:

  • The application of artificial intelligence (AI), specifically machine learning (ML), in oncology is rapidly expanding.
  • ML integration promises to accelerate diagnostic and treatment planning processes in cancer care.
  • The increasing volume of big data in healthcare necessitates advanced analytical tools like ML.

Purpose of the Study:

  • To review recent applications of machine learning in oncology.
  • To highlight ML's role in medical image analysis, treatment planning, prognosis, and drug synthesis.
  • To discuss the future prospects and ethical considerations of AI in cancer research and medicine.

Main Methods:

  • Review of recent literature on machine learning applications in oncology.
  • Analysis of ML's impact on diagnostic accuracy and treatment efficacy.
  • Exploration of ML's potential in drug discovery and personalized medicine.

Main Results:

  • Machine learning models have demonstrated improved prognostic prediction compared to traditional methods.
  • ML facilitates faster and more reliable analysis of medical images, crucial for aggressive cancers.
  • ML enhances the quality of prescribed treatments and patient care through big data analysis.
  • ML shows potential for direct synthesis of medical substances at the point of care.

Conclusions:

  • Machine learning is poised to become an essential technology for oncologists and medical specialists.
  • AI-driven tools offer significant potential for advancing cancer research and other medical fields.
  • Addressing unresolved ethical and legal issues is crucial for the widespread adoption of AI in medicine.