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

Increased expression of autophagy-related proteins in keratocystic odontogenic tumours: its possible association with growth potential.

The British journal of oral & maxillofacial surgery·2014
Same author

The sabotage of the bacterial transcription machinery by a small bacteriophage protein.

Bacteriophage·2014
Same author

In vivo and in vitro evidence of the sex-dependent pharmacokinetics and disposition of G004, a potential hypoglycemic agent, in rats.

European journal of drug metabolism and pharmacokinetics·2014
Same author

Inositol pyrophosphates mediate the effects of aging on bone marrow mesenchymal stem cells by inhibiting Akt signaling.

Stem cell research & therapy·2014
Same author

Sequential release of autophagy inhibitor and chemotherapeutic drug with polymeric delivery system for oral squamous cell carcinoma therapy.

Molecular pharmaceutics·2014
Same author

Proteomic analysis of differentially expressed skin proteins in iRhom2(Uncv) mice.

BMB reports·2014

Related Experiment Video

Updated: Jul 5, 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

A Deep Multi-Task Network to Learn Tumor Pathological Representations for Lymph Node Metastasis Prediction.

Danqing Hu1, Bing Liu2, Lechao Cheng1

  • 1Research Center for Intelligent Computing Software, Zhejiang Lab.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

Accurately predicting lymph node metastasis in non-small cell lung cancer is crucial. A novel multi-task network effectively uses primary tumor features to improve metastasis prediction, outperforming existing methods.

Keywords:
Multi-task learningdeep learninglymph node metastasis prediction

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.0K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Jul 5, 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: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.0K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Lymph node metastasis is critical for non-small cell lung cancer (NSCLC) patient management and prognosis.
  • Accurate preoperative assessment of lymph node metastasis remains a significant clinical challenge.

Purpose of the Study:

  • To develop and validate a multi-task deep learning network for predicting lymph node metastasis in NSCLC.
  • To leverage primary tumor pathological features, learned via pT stage prediction, to enhance lymph node metastasis prediction accuracy.

Main Methods:

  • A multi-task learning framework was designed to simultaneously predict pT stage and lymph node metastasis.
  • Electronic medical record data from 681 NSCLC patients were utilized for model training and evaluation.
  • Performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC) and Average Precision (AP).

Main Results:

  • The proposed multi-task network achieved an AUC of 0.768 (SD 0.073) and AP of 0.448 (SD 0.113) for lymph node metastasis prediction.
  • The method demonstrated significant performance improvement compared to baseline models.
  • The study confirmed the utility of learned tumor pathological representations for metastasis prediction.

Conclusions:

  • The developed multi-task network effectively predicts lymph node metastasis in NSCLC patients.
  • Integrating pT stage prediction aids in learning robust pathological features for improved metastasis assessment.
  • This approach offers a promising tool for enhancing preoperative diagnosis and patient care in NSCLC.