Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT

This study demonstrates that CBCT image quality significantly influences radiomic analysis, and deep learning-based enhancement techniques can effectively improve both image quality and the accuracy of radiomic features in NPC.

Authors

Chengjian Xiao (ORCID: https://orcid.org/0009-0000-5702-6082)
Weixiang Lin
Lipeng Liu (ORCID: https://orcid.org/0009-0007-1139-8076)
Feng Xie (ORCID: https://orcid.org/0000-0003-4867-6563)
Ying Xiao (ORCID: https://orcid.org/0000-0002-6855-0207)
Guangning Zheng

Institutions

Ganzhou People's Hospital (CN)

Publication Details

Journal: BMC Medical Imaging
Published: 2025-08-25
DOI: https://doi.org/10.1186/s12880-025-01894-9
Citations: 1
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 1.36

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article

Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT

Chengjian Xiao, Weixiang Lin, Lipeng Liu, Feng Xie et al.

Aug 25, 2025

1 citations

BMC Medical Imaging

Radiomics and Machine Learning in Medical Imaging

1.36

article

Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT

Chengjian Xiao, Weixiang Lin, Lipeng Liu, Feng Xie, Ying Xiao, Guangning Zheng

August 25, 2025article en

1 citations

Abstract

This study demonstrates that CBCT image quality significantly influences radiomic analysis, and deep learning-based enhancement techniques can effectively improve both image quality and the accuracy of radiomic features in NPC.

BMC Medical ImagingVol. 25(1)

Ganzhou People's Hospital (CN)

Openalex Percentile: Top 16%

Radiomics and Machine Learning in Medical Imaging

1.36

DOI Open Access PDF OpenAlex

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