Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features

被引:0
作者
Sui, Lianyu [1 ,4 ]
Meng, Huan [1 ,4 ]
Wang, Jianing [1 ,4 ]
Yang, Wei [2 ]
Yang, Lulu [1 ]
Chen, Xudan [2 ]
Zhuo, Liyong [1 ,4 ]
Xing, Lihong [1 ,4 ]
Zhang, Yu [1 ,4 ]
Cui, Jingjing [3 ]
Yin, Xiaoping [1 ,4 ]
机构
[1] Hebei Univ, Affiliated Hosp, Dept Radiol, Clin Med Sch, Baoding 071000, Peoples R China
[2] Baoding First Cent Hosp, Baoding 071000, Peoples R China
[3] United Imaging Intelligence Beijing Co Ltd, Yongteng North Rd, Beijing 100094, Peoples R China
[4] Hebei Key Lab Precise Imaging Inflammat Related Tu, Baoding 071000, Peoples R China
基金
国家重点研发计划;
关键词
Novel coronavirus pneumonia (NCP); Influenza A virus (IAV) pulmonary infection; Computed tomography (CT); Radiomics; Machine learning; COVID-19;
D O I
10.1186/s40537-024-01031-3
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
ObjectivesTo differentiate novel coronavirus pneumonia (NCP) with influenza A virus (IAV) pulmonary infection based on computed tomography (CT) radiomics features combined with clinical feature.MethodsA total of 292 patients were enrolled, as NCP determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings and IAV pulmonary infection confirmed by nucleic acid test with pneumonia lesion in the chest CT, retrospectively. The dataset was randomly divided into 233 cases in the training set and 59 cases in the validation set according to the ratio of 8:2, and there were 107 cases collected for verification as external test set. Firstly, voxel-based gray-level discretization (binWidth = 25) and Z-Score normalization were applied to preprocess the patient's ROI and normalize the extracted features. Then, the most predictive radiomic features were selected and their corresponding coefficients were evaluated using the correlation coefficient algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Subsequently, univariate logistic regression was employed to screen for clinically discriminative features from the patient's clinical characteristics. Finally, constructing the radiomics model and clinical model using support vector machines and logistic regression methods, respectively. And then combining these features of the two to construct a combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve were performed to evaluate the classification of the radiomics model, clinical model and combined model. Area under ROC curve (AUC) were calculated to evaluate the diagnostic efficiency, and Delong's test was used to compare the AUC between different models.ResultsAge, white blood cells, neutrophils, lymphocytes, and basic diseases reached statistical significance in the training set. After LASSO, 16 optimal radiomics features were retained. In the validation set and external test set, the SVM radiomics model achieved AUCs of 0.818 and 0.808 for automatic classification of NCP and IAV pulmonary infection,; and the clinical classification model shad AUCs were 0.676 and 0.669; finally, the 5 clinical features and the 16 selected radiomics features were used to construct the combined model with the AUCs of 0.821 and 0.820. After incorporating clinical features, the clinical model's discriminatory and predictive efficacy further improved in testing sets (AUC, 0.669 vs. 0.820, P = 0.002). The combined model performed well for differentiating the NCP and IAV pulmonary infection, and the calibration curves showed good agreement and decision curves indicated relatively satisfactory clinical benefits.ConclusionThe proposed combined model is feasible and effective in differentiating the NCP and IAV pulmonary infection, which may be used as a convenient and efficient auxiliary tool for radiologists to diagnose noninvasively based on the imaging structure of CT.
引用
收藏
页数:15
相关论文
共 28 条
[21]   Fatigue in radiology: a fertile area for future research [J].
Taylor-Phillips, Sian ;
Stinton, Chris .
BRITISH JOURNAL OF RADIOLOGY, 2019, 92 (1099)
[22]   Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia [J].
Wang, Hao ;
Wei, Ran ;
Rao, Guihua ;
Zhu, Jie ;
Song, Bin .
EUROPEAN RADIOLOGY, 2020, 30 (09) :4910-4917
[23]   uRP: An integrated research platform for one-stop analysis of medical images [J].
Wu, Jiaojiao ;
Xia, Yuwei ;
Wang, Xuechun ;
Wei, Ying ;
Liu, Aie ;
Innanje, Arun ;
Zheng, Meng ;
Chen, Lei ;
Shi, Jing ;
Wang, Liye ;
Zhan, Yiqiang ;
Zhou, Xiang Sean ;
Xue, Zhong ;
Shi, Feng ;
Shen, Dinggang .
FRONTIERS IN RADIOLOGY, 2023, 3
[24]   Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19 [J].
Wu, Zhiyuan ;
Li, Li ;
Jin, Ronghua ;
Liang, Lianchun ;
Hu, Zhongjie ;
Tao, Lixin ;
Han, Yong ;
Feng, Wei ;
Zhou, Di ;
Li, Weiming ;
Lu, Qinbin ;
Liu, Wei ;
Fang, Liqun ;
Huang, Jian ;
Gu, Yu ;
Li, Hongjun ;
Guo, Xiuhua .
EUROPEAN JOURNAL OF RADIOLOGY, 2021, 137
[25]   Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis [J].
Xiao, Anling ;
Zhao, Huijuan ;
Xia, Jianbing ;
Zhang, Ling ;
Zhang, Chao ;
Ruan, Zhuoying ;
Mei, Nan ;
Li, Xun ;
Ma, Wuren ;
Wang, Zhuozhu ;
He, Yi ;
Lee, Jimmy ;
Zhu, Weiming ;
Tian, Dajun ;
Zhang, Kunkun ;
Zheng, Weiwei ;
Yin, Bo .
FRONTIERS IN MEDICINE, 2021, 8
[26]   Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features [J].
Yang, Zhiqi ;
Lin, Daiying ;
Chen, Xiaofeng ;
Qiu, Jinming ;
Li, Shengkai ;
Huang, Ruibin ;
Yang, Zhijian ;
Sun, Hongfu ;
Liao, Yuting ;
Xiao, Jianning ;
Tang, Yanyan ;
Chen, Xiangguang ;
Zhang, Sheng ;
Dai, Zhuozhi .
FRONTIERS IN MICROBIOLOGY, 2022, 13
[27]   Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia [J].
Zhou, Min ;
Yang, Dexiang ;
Chen, Yong ;
Xu, Yanping ;
Xu, Jin-Fu ;
Jie, Zhijun ;
Yao, Weiwu ;
Jin, Xiaoyan ;
Pan, Zilai ;
Tan, Jingwen ;
Wang, Lan ;
Xia, Yihan ;
Zou, Longkuan ;
Xu, Xin ;
Wei, Jingqi ;
Guan, Mingxin ;
Yan, Fuhua ;
Feng, Jianxing ;
Zhang, Huan ;
Qu, Jieming .
ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (02)
[28]   Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches [J].
Zorzi, Giulia ;
Berta, Luca ;
Rizzetto, Francesco ;
De Mattia, Cristina ;
Felisi, Marco Maria Jacopo ;
Carrazza, Stefano ;
Nerini Molteni, Silvia ;
Vismara, Chiara ;
Scaglione, Francesco ;
Vanzulli, Angelo ;
Torresin, Alberto ;
Colombo, Paola Enrica .
EUROPEAN RADIOLOGY EXPERIMENTAL, 2023, 7 (01)