CT radiomics to assess severity of explosion-induced primary blast lung injury in goats

Previous studies on primary blast lung injury have mostly been small-sample simulation experiments, primarily relying on morphological identification and lacking imaging-based classification of severity. Herein, we have established a large-sample model of goats exposed to real natural field explosions and employed CT radiomics to assess the severity of lung injury. By extracting 1288 radiomics features and combining baseline data, baseline, radiomics, and comprehensive models were built. Results showed that the radiomics and comprehensive models outperformed the baseline model. Decision curve analysis indicated better clinical benefits with models incorporating rad-scores. A nomogram established with multiple factors demonstrated individualized predictive performance. The addition of CT radiomics features improved assessment accuracy and is expected to support clinical decision-making.

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Publication Details

Journal
Scientific Reports
Published
2025-06-02
DOI
https://doi.org/10.1038/s41598-025-03069-6
Primary Topic
Disaster Response and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

CT radiomics to assess severity of explosion-induced primary blast lung injury in goats

Kuijun Chen, Y Yao, Bo Yang, Linlan Zeng et al.
Scientific Reports
Disaster Response and Management
article

CT radiomics to assess severity of explosion-induced primary blast lung injury in goats

Kuijun Chen, Y Yao, Bo Yang, Linlan Zeng, Ping Xiang, Shunan Wang, Zhaoxia Duan, Ming Li, Jianmin Wang, Feng Ling, Hanwei Wang, Kunlin Xiong
article en

Abstract

Previous studies on primary blast lung injury have mostly been small-sample simulation experiments, primarily relying on morphological identification and lacking imaging-based classification of severity. Herein, we have established a large-sample model of goats exposed to real natural field explosions and employed CT radiomics to assess the severity of lung injury. By extracting 1288 radiomics features and combining baseline data, baseline, radiomics, and comprehensive models were built. Results showed that the radiomics and comprehensive models outperformed the baseline model. Decision curve analysis indicated better clinical benefits with models incorporating rad-scores. A nomogram established with multiple factors demonstrated individualized predictive performance. The addition of CT radiomics features improved assessment accuracy and is expected to support clinical decision-making.

Scientific ReportsVol. 15(1)
Army Medical University (CN), Daping Hospital (CN), Army Sustainment University (US), Chongqing Emergency Medical Center (CN), First People's Hospital of Chongqing (CN), Gansu Provincial Center for Disease Control and Prevention (CN), General Hospital of Central Theater Command
Chongqing Science and Technology Commission, Army Medical University
Openalex Percentile: Top 79%
Disaster Response and Management
DOIOpen Access PDFOpenAlex
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