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

321
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...
321
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.8K
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...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Real-World Data on the Use of Nivolumab Monotherapy in the Treatment of Advanced Renal Cell Carcinoma After Prior Therapy: Final Results from the Non-Interventional NORA Study.

Cancers·2026
Same author

[New developments in endocrinology/diabetology].

Innere Medizin (Heidelberg, Germany)·2026
Same author

Erdafitinib in Patients With Advanced Solid Tumors With <i>FGFR</i> Alterations (RAGNAR): Results of Tumor-Specific Analyses and Secondary Cohorts.

JCO precision oncology·2026
Same author

Prospective randomized analysis of operating time and safety of laparoscopic pyeloplasty with or without TriSect rapide® in adult patients with ureteropelvic junction obstruction.

BMC urology·2026
Same author

Confirmation of Prostate-specific Antigen (PSA) Values Improves Prostate Cancer Screening in Early Middle-Aged Men - Data from the PROBASE Trial.

European urology·2026
Same author

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

PLOS digital health·2026

Related Experiment Video

Updated: May 30, 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

8.2K

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.

Julius Keyl1,2, Philipp Keyl3,4, Grégoire Montavon4,5,6

  • 1Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany.

Nature Cancer
|January 30, 2025
PubMed
Summary

Explainable AI (xAI) identifies key markers and interactions from multimodal data to improve cancer patient outcome prediction. This approach enhances clinical decision-making and supports personalized, data-driven cancer care.

More Related Videos

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.9K

Related Experiment Videos

Last Updated: May 30, 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

8.2K
Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.9K

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Precision oncology currently relies on limited clinical variables and expert knowledge for decision-making.
  • There is a need for advanced methods to integrate diverse patient data for improved cancer care.

Purpose of the Study:

  • To introduce AI-derived (AID) markers using explainable artificial intelligence (xAI) for clinical decision support.
  • To decode patient outcomes by analyzing multimodal real-world data and identifying key prognostic markers and their interactions.

Main Methods:

  • Utilized xAI to analyze data from 15,726 patients across 38 solid cancer types.
  • Integrated 350 markers including clinical records, image-derived body compositions, and mutational tumor profiles.
  • Validated the approach in an independent cohort of 3,288 lung cancer patients from electronic health records.

Main Results:

  • Identified 114 key markers contributing to 90% of the neural network's decision process.
  • Uncovered 1,373 prognostic interactions between these markers.
  • Demonstrated the model's predictive capability in a validation cohort.

Conclusions:

  • xAI can effectively decode complex patient data to identify significant prognostic markers and interactions.
  • This approach has the potential to transform clinical variable assessment in oncology.
  • Enables more personalized and data-driven cancer care through enhanced decision support.