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

  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Prediction Of Epidermal Growth Factor Receptor (egfr) Mutation Status In Lung Adenocarcinoma Patients On Computed Tomography (ct) Images Using 3-dimensional (3d) Convolutional Neural Network

Prediction of epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients on computed tomography (CT) images using 3-dimensional (3D) convolutional neural network

Guojin Zhang1, Lan Shang1, Yuntai Cao2

  • 1Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Quantitative Imaging in Medicine and Surgery
|August 15, 2024

Related Experiment 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: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.2K

View abstract on PubMed

Summary
This summary is machine-generated.

A deep learning model using computed tomography (CT) images accurately predicts epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. This noninvasive approach aids in selecting targeted therapy.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Noninvasive detection of epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients is crucial for guiding targeted therapy.
  • Current methods face challenges in accurately predicting EGFR mutation status prior to treatment initiation.

Purpose of the Study:

  • To develop and evaluate a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model for predicting EGFR mutation status using computed tomography (CT) images.
  • To compare the performance of the CNN model against clinical and radiomics models.

Main Methods:

  • A retrospective cohort of 660 lung adenocarcinoma patients was analyzed, with data split into training (n=528) and external test (n=132) sets.
  • A supervised, end-to-end trained 3D CNN model was developed and validated on the external test set.
Keywords:
Deep learningcomputed tomography (CT)convolutional neural network (CNN)epidermal growth factor receptor (EGFR)

Related Experiment 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: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.2K
  • Performance was assessed using receiver operating characteristic (ROC) curves and compared with clinical and radiomics models, including a comprehensive model combining CNN and radiomics features.

    • Main Results:

    • The CNN model demonstrated superior predictive performance, achieving an area under the curve (AUC) of 94.7% (95% CI, 0.894-0.978) on the test set.
    • This significantly outperformed clinical (AUC=68.4%) and radiomics models (AUC=72.4%).
    • The CNN model also showed better stability compared to a comprehensive model integrating CNN and radiomics features.

    Conclusions:

    • The developed 3D CNN model exhibits excellent performance in noninvasively predicting EGFR mutation status in lung adenocarcinoma.
    • This AI-driven approach shows potential as an auxiliary tool for clinicians in treatment decision-making.
    • Further integration into clinical workflows could improve patient management and targeted therapy selection.
    lung adenocarcinoma