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

Evaluation of a Retrieval-Augmented Generation-Based Large Language Model for Evidence-Based Herb and Supplement Information in Cancer Care.

JMIR cancer·2026
Same author

Benralizumab relieves Eosinophil-Related Cutaneous Adverse Events from Cancer Therapy: A Nonrandomized Phase 2 Trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Associations Between Anxiety or Depression Diagnosis and Immune Checkpoint Inhibitor Outcomes.

Cancer medicine·2025
Same author

Postimmunotherapy and Vascular Endothelial Growth Factor Inhibitor Landscape in Advanced Renal Cell Carcinoma: Treatment Patterns, Costs, and Outcomes.

JCO oncology practice·2025
Same author

Pragmatic Use of Minimal Common Oncology Data Elements and Observational Medical Outcomes Partnership at an Academic Medical Center.

JCO clinical cancer informatics·2025
Same author

Phase 1b/2 study of batiraxcept alone and in combination with cabozantinib with or without nivolumab for advanced clear cell renal cell carcinoma.

The oncologist·2025

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

Artificial Intelligence-Assisted Cancer Status Detection in Radiology Reports.

Ankur Arya1, Andrew Niederhausern2, Nadia Bahadur3

  • 1Digital, Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, New York.

Cancer Research Communications
|April 9, 2024
PubMed
Summary

Artificial intelligence (AI) and natural language processing (NLP) models accelerate cancer research by automating radiology report annotation. This AI-driven approach enhances data accuracy and efficiency, reducing manual curation time and costs.

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication
07:47

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

684

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
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication
07:47

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

684

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Oncology
  • Radiology Data Analytics

Background:

  • Radiology reports contain valuable patient data for cancer research but lack structured formats.
  • Manual curation of these reports is time-consuming, costly, and hinders research progress.
  • Existing manual methods are sensitive to data volume, complexity, and require specialized skills.

Purpose of the Study:

  • To develop and implement an AI-powered pipeline for efficient and accurate annotation of radiology reports.
  • To leverage natural language processing (NLP) models for extracting critical data elements from unstructured text.
  • To improve the speed, accuracy, and cost-effectiveness of data curation for cancer research.

Main Methods:

  • Exploration of state-of-the-art AI techniques for automated data extraction.
  • Implementation of an end-to-end pipeline for radiology report annotation.
  • Training language models (LMs) as multiclass or multilabel classifiers to predict imaging sites, cancer presence, and status.

Main Results:

  • Trained NLP models achieved high weighted F1 scores and accuracy in classification tasks.
  • The AI-assisted curation process demonstrated increased accuracy and F1 scores compared to manual methods.
  • Significant reduction in time and cost associated with data curation was observed.

Conclusions:

  • AI and NLP models offer a faster and more accurate alternative to manual data extraction from radiology reports.
  • Automated annotation assists manual curation, improving overall efficiency and reducing costs.
  • This approach accelerates cancer research by enabling quicker access to structured patient data.