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

311
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...
311
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

99
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...
99
Hazard Ratio01:12

Hazard Ratio

72
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
72
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

69
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
69
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

Stress and risk of breast cancer; findings from a large population-based incident case-control study.

Scientific reports·2026
Same author

Prevalence of sarcopenia and population attributable fraction of related factors in Iranian older adults from the IMOS‑2021 study.

Scientific reports·2026
Same author

Levels of bone formation marker P1NP in individuals over 50 years: a systematic review and meta-analysis.

Journal of diabetes and metabolic disorders·2026
Same author

Pharmacotherapeutic Management of Depression in Patients With Cancer: A Review of Mechanistic and Clinical Evidence.

Cancer reports (Hoboken, N.J.)·2026
Same author

Dietary inflammatory index and quality of life in patients with breast cancer: a cross-sectional study.

Journal of health, population, and nutrition·2026
Same author

Immune Checkpoint Blockade and Emerging Combination Platforms in Breast Cancer: A Narrative Review.

Breast cancer (Dove Medical Press)·2026

Related Experiment Video

Updated: May 17, 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

6.6K

Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data.

Haniyeh Rafiepoor1, Alireza Ghorbankhanloo1, Kazem Zendehdel1

  • 1Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.

Cancer Reports (Hoboken, N.J.)
|April 3, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models did not significantly improve breast cancer (BC) risk prediction over the traditional Gail model. Enhancing models with genetic and image data is key for future accuracy in BC risk assessment.

Keywords:
artificial intelligencebreast cancerconventional modelsmachine learningrisk assessment

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
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

187

Related Experiment Videos

Last Updated: May 17, 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

6.6K
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
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

187

Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Breast cancer (BC) poses a significant global health challenge, particularly in developing nations.
  • Traditional risk assessment models like the Gail model have limitations due to linear assumptions.
  • Advancements in artificial intelligence (AI) offer new approaches for complex medical risk prediction.

Purpose of the Study:

  • To compare AI-based models against the traditional Gail model for breast cancer risk assessment.
  • To evaluate the predictive performance of AI models in a population dataset.
  • To analyze the accuracy, sensitivity, and precision of different risk prediction models.

Main Methods:

  • Utilized a dataset of 942 newly diagnosed breast cancer patients and 975 healthy controls.
  • Applied ten distinct classification algorithms from machine learning.
  • Assessed and compared accuracy, sensitivity, precision, and feature importance of AI algorithms.

Main Results:

  • AI algorithms alone did not demonstrate a significant improvement in breast cancer risk predictability compared to the Gail model.
  • Variable importance differed substantially across various AI algorithms.
  • Understanding feature importance and interactions is critical for improving AI model accuracy.

Conclusions:

  • Incorporating specific risk factors like genetic and image-related variables is essential for enhancing breast cancer risk prediction models.
  • Addressing modeling limitations with restricted features is crucial for future research in breast cancer risk assessment.