Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Understanding mechanisms of combined polystyrene and cadmium toxicity in <i>Leonurus japonicus</i> Houtt. across varying pH levels.

RSC advances·2026

Same author

Towards the construction of a virtual yeast.

Nature·2026

Same author

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Nature communications·2026

Same author

MOCVD-grown n-InAs/GaAs heterostructures for tunable mid-IR magneto-optical nonreciprocity.

Optics express·2026

Same author

Determination of fluorinated pesticides in food products: Current advancements and prospective challenges.

Food chemistry·2026

Same author

Intracellular Proton Enrichment Drives Unified Dual-Cofactor Regeneration for Biohybrid CO<sub>2</sub> Fixation.

Angewandte Chemie (International ed. in English)·2026

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound

Pengcheng Shen¹, Zheyu Yang², Jingjing Sun³

¹Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China.

Nature Communications

|August 1, 2025

Summary

A new deep-learning model, LLNM-Net, accurately predicts lateral lymph node metastasis using multimodal data. This tool aids surgical planning and prognosis by identifying high-risk patients with superior accuracy.

More Related Videos

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Sep 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Accurate preoperative prediction of lateral lymph node metastasis is vital for thyroid cancer management.
Current prediction methods lack precision, impacting surgical strategies and patient prognosis.

Purpose of the Study:

To develop and validate a deep-learning model for precise preoperative prediction of lateral lymph node metastasis.
To integrate multimodal data for enhanced diagnostic accuracy.

Main Methods:

Developed the Lateral Lymph Node Metastasis Network (LLNM-Net), a bidirectional-attention deep-learning model.
Fused multimodal data from 29,615 patients and 9836 surgical cases across seven centers.
Integrated nodule morphology, position, clinical text, and demographics.

Main Results:

LLNM-Net achieved an Area Under the Curve (AUC) of 0.944 and 84.7% accuracy in multicenter testing.
The model outperformed human experts (64.3% accuracy) and previous models by 7.4%.
Tumors near the thyroid capsule (>72% metastasis risk) and specific lobes were identified as high-risk regions.

Conclusions:

LLNM-Net demonstrates significant potential for preoperative screening and risk stratification of lateral lymph node metastasis.
The model's ability to integrate diverse data sources enhances its predictive power.
LLNM-Net offers a promising tool to improve surgical decision-making and patient outcomes.