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

Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node Metastasis Prediction: Development of a Knowledge-Augmented Framework.

JMIR medical informatics·2026
Same author

A Large Language Model-Powered Multiagent Framework Emulating Standardized Patients in Clinical Communication Skills Training: Development and Evaluation Study.

Journal of medical Internet research·2026
Same author

Need Analysis of Clinician-Oriented Integrated Precision Oncology Decision Support Tools: Qualitative Descriptive Study.

JMIR human factors·2025
Same author

Correction: Dysregulated ceramides metabolism by fatty acid 2-hydroxylase exposes a metabolic vulnerability to target cancer metastasis.

Signal transduction and targeted therapy·2025
Same author

Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study.

Journal of medical Internet research·2025
Same author

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

JCO precision oncology·2024

Related Experiment Video

Updated: Sep 25, 2025

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

208

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small

Danqing Hu1, Shaolei Li2, Huanyao Zhang1

  • 1College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.

JMIR Medical Informatics
|April 25, 2022
PubMed
Summary

This study developed machine learning models using natural language processing (NLP) to predict lymph node metastasis (LNM) in non-small cell lung cancer patients. The models demonstrated superior performance compared to traditional methods, improving diagnostic accuracy.

Keywords:
algorithmdecision makingelectronic medical recordsforest modelinglung cancerlymph node metastasis predictionmachine learningnatural language processingnon–small cell lung cancerprediction models

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

7.0K
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

Related Experiment Videos

Last Updated: Sep 25, 2025

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

208
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

7.0K
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

Area of Science:

  • Oncology
  • Medical Informatics
  • Computational Biology

Background:

  • Lymph node metastasis (LNM) is crucial for resectable non-small cell lung cancer (NSCLC) treatment decisions.
  • Preoperative diagnosis of LNM is challenging, hindering optimal treatment planning.
  • Electronic medical records (EMRs) contain valuable LNM data, but unstructured text limits its utility.

Purpose of the Study:

  • To develop predictive models for LNM using EMR data.
  • To leverage natural language processing (NLP) and machine learning (ML) for feature extraction from clinical notes.
  • To improve the accuracy of preoperative LNM staging in NSCLC.

Main Methods:

  • A multiturn question answering NLP model was created to extract tumor and lymph node features from CT reports.
  • Extracted features were combined with structured clinical data for ML-based LNM prediction.
  • Models were evaluated against CT size criteria and clinician assessments, with concordance correlation used to assess NLP feature extraction accuracy.

Main Results:

  • Random forest models achieved high performance: AUC 0.792 (pN2) and 0.768 (pN1&N2).
  • ML models significantly outperformed size criteria and clinician evaluations.
  • High concordance (0.950) between NLP-extracted and gold standard features was observed, improving to 0.984 with top feature refinement.

Conclusions:

  • Developed LNM prediction models offer competitive performance using limited EMR data (CT reports, tumor markers).
  • The NLP model effectively extracts features, supporting the development and clinical application of predictive LNM models.
  • This approach enhances preoperative LNM assessment accuracy for NSCLC patients.