Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules

The integration of Rad and 2.5D DTL significantly improves the diagnostic accuracy for differentiating between benign and malignant Lung-RADS 3 and 4A nodules. This approach provides a robust decision-support tool that could potentially reduce unnecessary interventions for benign nodules while facilitating earlier detection and treatment of malignant lesions.

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Publication Details

Journal
Journal of Thoracic Disease
Published
2025-10-01
DOI
https://doi.org/10.21037/jtd-2025-584
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules

Xingyu Mu, Guanqiao Jin, Kui Wang, Lijuan Liu et al.
Journal of Thoracic Disease
Lung Cancer Diagnosis and Treatment
article

Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules

Xingyu Mu, Guanqiao Jin, Kui Wang, Lijuan Liu, Xiaoxiao Huang, Xiaoxin Huang
article en

Abstract

The integration of Rad and 2.5D DTL significantly improves the diagnostic accuracy for differentiating between benign and malignant Lung-RADS 3 and 4A nodules. This approach provides a robust decision-support tool that could potentially reduce unnecessary interventions for benign nodules while facilitating earlier detection and treatment of malignant lesions.

Journal of Thoracic DiseaseVol. 17(10)
Guangxi Medical University (CN), Guilin Medical University (CN), Affiliated Hospital of Youjiang Medical University for Nationalities (CN)
Openalex Percentile: Top 54%
Lung Cancer Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules — Xingyu Mu, Guanqiao Jin, et al. · Journal of Thoracic Disease (2025) | TGRS Research Map | TGRS