Deep learning-based automated segmentation and volumetric measurement improve inter-observer consistency and reduce false-positive findings in Lung-RADS assessment of solid pulmonary nodules

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Publication Details

Journal
BMC Cancer
Published
2026-11-03
DOI
https://doi.org/10.1186/s12885-026-17166-w
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep learning-based automated segmentation and volumetric measurement improve inter-observer consistency and reduce false-positive findings in Lung-RADS assessment of solid pulmonary nodules

Yuxuan Han, Wu Xiaoming, Nana Zhang, Yuting Xie et al.
BMC Cancer
Lung Cancer Diagnosis and Treatment
article

Deep learning-based automated segmentation and volumetric measurement improve inter-observer consistency and reduce false-positive findings in Lung-RADS assessment of solid pulmonary nodules

Yuxuan Han, Wu Xiaoming, Nana Zhang, Yuting Xie, Li Hongjia, Wu Quanyang, Han Shiyi, Qu Xiaofeng, Xu Yuxin, Mei lingjun
article en

Abstract

No abstract available for this paper.
BMC Cancer
Dalian Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beijing Tian Tan Hospital (CN), Siemens (China) (CN), Second Affiliated Hospital of Dalian Medical University (CN), Beijing Anding Hospital (CN), Cancer Hospital of Chinese Academy of Medical Sciences (CN), Siemens Healthcare (United States) (US)
Openalex Percentile: Top 13%
Lung Cancer Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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Deep learning-based automated segmentation and volumetric measurement improve inter-observer consistency and reduce false-positive findings in Lung-RADS assessment of solid pulmonary nodules — Yuxuan Han, Wu Xiaoming, et al. · BMC Cancer (2026) | TGRS Research Map | TGRS