Yang Bo, Wang Hanwei, Feng Ling, Li Ming, Zeng Linlan, Xiang Ping, Yao Yishan, Chen Kuijun, Duan Zhaoxia, Wang Jianmin, Xiong Kunlin, Wang Shunan
Department of Radiology, Daping Hospital, Army Medical University, NO.10, Changjiang Road, YuZhong District, Chongqing, China.
Chongqing Imaging and Nuclear Medicine Clinical Medical Research Center, Chongqing, China.
Sci Rep. 2025 Jun 2;15(1):19280. doi: 10.1038/s41598-025-03069-6.
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.
以往关于原发性爆震性肺损伤的研究大多是小样本模拟实验，主要依靠形态学识别，缺乏基于影像学的严重程度分类。在此，我们建立了山羊暴露于真实野外爆炸的大样本模型，并采用CT放射组学评估肺损伤的严重程度。通过提取1288个放射组学特征并结合基线数据，构建了基线、放射组学和综合模型。结果表明，放射组学模型和综合模型优于基线模型。决策曲线分析表明，纳入放射学评分的模型具有更好的临床效益。由多个因素建立的列线图显示出个性化的预测性能。CT放射组学特征的加入提高了评估准确性，有望支持临床决策。
