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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
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...
5.1K
Nursing Clinical Information System01:27

Nursing Clinical Information System

876
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
876
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.8K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Communication With Clinicians and Relatives About Cascade Genetic Testing in Cancer Patients With Germline Pathogenic Variants.

JCO precision oncology·2026
Same author

CLEAR-AI: confounder-aware learning for equitable and accurate reasoning in AI for diagnosis.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Mapping the contributions of adjuvant tamoxifen, 20 years later.

Journal of the National Cancer Institute·2026
Same author

Multimodal artificial intelligence models for radiology.

BJR artificial intelligence·2026
Same author

Preclinical circulating tumor DNA shedding duration and prognostic implications of modeling 3669 patients with cancer in the American Cancer Society Cancer Prevention Study-3 and Circulating Cell-Free Genome Atlas Substudy 3.

Cancer·2026
Same author

Evaluating Tumor Burden as a Predictive Biomarker for Epidermal Growth Factor Receptor Targeted Kinase Inhibitor Therapy in Advanced Non-Small Cell Lung Cancer.

JCO precision oncology·2026
Same journal

Bayesian Methods for Subgroup Efficacy and Safety: Application to Japanese Patients in JAVELIN Renal 101.

JCO clinical cancer informatics·2026
Same journal

Effect of a Multidimensional Digital Health Intervention on Quality of Life in Breast Cancer Survivors: A Randomized Controlled Trial.

JCO clinical cancer informatics·2026
Same journal

Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?

JCO clinical cancer informatics·2026
Same journal

Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers.

JCO clinical cancer informatics·2026
Same journal

Foundation Model-Driven Regions of Interest Classification and Renaming in Cancer Radiotherapy: A Customizable, Retraining-Free Workflow Across Institutions.

JCO clinical cancer informatics·2026
Same journal

Announcing a New Article Type in <i>JCO Clinical Cancer Informatics</i>: The Resource Report.

JCO clinical cancer informatics·2026
See all related articles

Related Experiment Video

Updated: Sep 17, 2025

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

9.1K

Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From

Amara Tariq1, Madhu Sikha1, Allison W Kurian2

  • 1Department of Radiology, Mayo Clinic, Phoenix, AZ.

JCO Clinical Cancer Informatics
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for automated breast cancer treatment data extraction, improving accuracy and efficiency. The developed model effectively extracts longitudinal treatment timelines from clinical notes, aiding cancer research.

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

8.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

692

Related Experiment Videos

Last Updated: Sep 17, 2025

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

9.1K
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

8.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

692

Area of Science:

  • Medical Informatics
  • Oncology
  • Natural Language Processing

Background:

  • Automated curation of breast cancer treatment data is crucial for evidence-based patient management and treatment pathway assessment.
  • Challenges include complex, inconsistent clinical data and extracting information from free-text narratives.

Purpose of the Study:

  • To develop and validate a hybrid information extraction framework for automated, minimal-human-involvement curation of breast cancer treatment data.
  • To accurately extract longitudinal treatment timelines from time-stamped clinical notes.

Main Methods:

  • A hybrid, two-phase information extraction framework combining a Unified Medical Language System parser and a fine-tuned large language model (LLM).
  • The framework was trained end-to-end as a question-answering model to identify specific cancer treatments.
  • Internal validation on 26,692 patients and external validation on 162 patients from different institutions.

Main Results:

  • The proposed model achieved an average AUROC of 0.942 (internal) and 0.924 (external).
  • Demonstrated a superior trade-off between sensitivity (79.2%) and specificity (76.2%) compared to rule-based and structured code methods.
  • Out-of-the-box LLMs showed high specificity but low sensitivity for targeted clinical tasks.

Conclusions:

  • The framework effectively extracts temporal cancer treatment information from diverse clinical notes, irrespective of treatment setting or time frame.
  • The code is packaged as a Docker image for easy deployment and shared under an open-source academic license to support the research community.