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

576
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...
576
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.3K
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,...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Solution-solid-solid mechanism: superionic conductors catalyze nanowire growth.

Nano letters·2013
Same author

Depending on the stage of hepatosteatosis, p53 causes apoptosis primarily through either DRAM-induced autophagy or BAX.

Liver international : official journal of the International Association for the Study of the Liver·2013
Same author

Low-voltage switching of crease patterns on hydrogel surfaces.

Advanced materials (Deerfield Beach, Fla.)·2013
Same author

Long non-coding RNAs and prostate cancer.

Journal of nanoscience and nanotechnology·2013
Same author

Self-assembled graphene quantum dots induced by cytochrome c: a novel biosensor for trypsin with remarkable fluorescence enhancement.

Nanoscale·2013
Same author

Relationship between glutathione S-transferase P1 (GSTP1), X-ray repair cross complementing group 1 (XRCC1) and 5,10-methylenetetrahydrofolate reductase (5,10-MTHFR) gene polymorphisms and response to chemotherapy in advanced gastric cancer.

Onkologie·2013
Same journal

BlockFedMed: A blockchain-federated learning framework for privacy-preserving mortality prediction across heterogeneous intensive care units.

International journal of medical informatics·2026
Same journal

Integrating clinical decision support systems in pediatric oncology: A scoping review of applications, implementation gaps, and management Implications.

International journal of medical informatics·2026
Same journal

Understanding digital health capability of allied health professionals - a mixed-methods study with content validity analysis.

International journal of medical informatics·2026
Same journal

On-premises open-source large language models for privacy-preserving multimodal depression screening.

International journal of medical informatics·2026
Same journal

Data mining methods, tasks, and algorithms for adverse drug reaction analysis in pharmacovigilance: A scoping review.

International journal of medical informatics·2026
Same journal

Development and validation of an interpretable machine learning model for predicting systemic inflammatory response syndrome after percutaneous nephrolithotomy: A multicenter study.

International journal of medical informatics·2026
See all related articles

Related Experiment Video

Updated: Dec 20, 2025

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

8.6K

Using machine learning to predict ovarian cancer.

Mingyang Lu1, Zhenjiang Fan2, Bin Xu1

  • 1Department of Tumor Biological Treatment, the Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, People's Republic of China; Jiangsu Engineering Research Center for Tumor Immunotherapy, Changzhou, Jiangsu, People's Republic of China; Institute of Cell Therapy, Soochow University, Changzhou, Jiangsu, People's Republic of China.

International Journal of Medical Informatics
|June 3, 2020
PubMed
Summary
This summary is machine-generated.

A machine learning model using human epididymis protein 4 (HE4) and carcinoembryonic antigen (CEA) effectively predicts ovarian cancer (OC). This simple, two-biomarker approach outperforms existing methods for classifying benign ovarian tumors and OC.

Keywords:
Machine LearningOvarian CancerTumor Marker

More Related Videos

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

7.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

422

Related Experiment Videos

Last Updated: Dec 20, 2025

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

8.6K
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

7.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

422

Area of Science:

  • Oncology
  • Biomedical Informatics
  • Machine Learning

Background:

  • Ovarian cancer (OC) is a leading cause of cancer-related deaths in women.
  • Accurate differentiation between benign ovarian tumors (BOT) and OC is crucial for timely and appropriate treatment.
  • Existing diagnostic methods for ovarian cancer can be complex and may lack sufficient accuracy.

Purpose of the Study:

  • To develop a simple yet accurate predictive model for classifying benign ovarian tumors (BOT) and ovarian cancer (OC).
  • To leverage machine learning for identifying key biomarkers in ovarian cancer prediction.
  • To compare the performance of the developed model against established methods like ROMA.

Main Methods:

  • A dataset of 349 Chinese patients with 49 variables was utilized.
  • Minimum Redundancy - Maximum Relevance (MRMR) feature selection was applied to identify relevant features from 235 patients.
  • A decision tree model was constructed using selected features and validated on remaining patients, comparing results with ROMA and logistic regression.

Main Results:

  • Eight significant features were identified by MRMR, with human epididymis protein 4 (HE4) and carcinoembryonic antigen (CEA) emerging as top predictors.
  • The decision tree model demonstrated superior predictive performance compared to the Risk of Ovarian Malignancy Algorithm (ROMA).
  • CEA proved particularly valuable for OC prediction in cases with low HE4 levels.

Conclusions:

  • Machine learning effectively classifies benign ovarian tumors and ovarian cancer.
  • A simple two-biomarker model (HE4 and CEA) demonstrates high predictive accuracy and interpretability.
  • This machine learning-driven approach shows significant potential for improving ovarian cancer prediction and outperforms current methods.