A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.

Journal: Annals of medicine

Published Date: Sep 16, 2024

Abstract

OBJECTIVE: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB).

Authors

Lihong Zhou

School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Yiwen Wang
Wenchao Zhu
Yafang Zhao

Zhejiang Tuberculosis Diagnosis and Treatment Center, Zhejiang Chinese and Western Medicine Integrated Hospital, Hangzhou, Zhejiang, China.
Yihang Yu

Zhejiang Tuberculosis Diagnosis and Treatment Center, Zhejiang Chinese and Western Medicine Integrated Hospital, Hangzhou, Zhejiang, China.
Qin Hu

School of pharmacy, Nanjing medical university, Nanjing, Jiangsu, 211166, People's Republic of China. huqin@njmu.edu.cn.
Wenke Yu

Department of Radiology, Qingchun Hospital of Zhejiang Province, Hangzhou, China.

Keywords

Adult Aged Algorithms Diagnosis, Differential Female Humans Machine Learning Male Middle Aged Mycobacterium Infections, Nontuberculous Radiomics Retrospective Studies ROC Curve Support Vector Machine Tomography, X-Ray Computed Tuberculosis, Pulmonary

External Resources

View on PubMed Access via DOI PubMed (39283049)

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals