Artificial Intelligence Algorithm Can Predict Lymph Node Malignancy from Endobronchial Ultrasound Transbronchial Needle Aspiration Images for Non-Small Cell Lung Cancer.

Journal: Respiration; international review of thoracic diseases

PMID: 39278204

Abstract

INTRODUCTION: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for lung cancer staging is operator dependent, resulting in high rates of non-diagnostic lymph node (LN) samples. We hypothesized that an artificial intelligence (AI) algorithm can consistently and reliably predict nodal metastases from the ultrasound images of LNs when compared to pathology.

Authors

Yogita S Patel

Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada, patelys@mcmaster.ca.
Anthony A Gatti

McMaster University and NeuralSeg, Hamilton, Ontario, Canada.
Forough Farrokhyar

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Feng Xie

School of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: 1111705006@mail2.gdut.edu.cn.
Waël C Hanna

Division of Thoracic Surgery, McMaster University, Hamilton, Canada.

Keywords

Aged Algorithms Artificial Intelligence Carcinoma, Non-Small-Cell Lung Endoscopic Ultrasound-Guided Fine Needle Aspiration Female Humans Lung Neoplasms Lymph Nodes Lymphatic Metastasis Male Middle Aged Neoplasm Staging Neural Networks, Computer Predictive Value of Tests

External Resources

View on PubMed Access via DOI PubMed (39278204)

Artificial Intelligence Algorithm Can Predict Lymph Node Malignancy from Endobronchial Ultrasound Transbronchial Needle Aspiration Images for Non-Small Cell Lung Cancer.

Abstract

Authors

Keywords

External Resources

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