Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

作者全名："Ding, Yu; Zhang, Jingyu; Zhuang, Weitao; Gao, Zhen; Kuang, Kaiming; Tian, Dan; Deng, Cheng; Wu, Hansheng; Chen, Rixin; Lu, Guojie; Chen, Gang; Mendogni, Paolo; Migliore, Marcello; Kang, Min-Woong; Kanzaki, Ryu; Tang, Yong; Yang, Jiancheng; Shi, Qiuling; Qiao, Guibin"

作者地址："[Ding, Yu; Zhuang, Weitao; Tian, Dan; Deng, Cheng; Chen, Gang; Tang, Yong; Qiao, Guibin] Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Thorac Surg, 106 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China; [Ding, Yu; Gao, Zhen; Wu, Hansheng; Qiao, Guibin] Southern Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China; [Zhang, Jingyu; Shi, Qiuling] Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, 1 Med Coll Rd, Chongqing 400016, Peoples R China; [Kuang, Kaiming; Yang, Jiancheng] Dianei Technol, Shanghai, Peoples R China; [Wu, Hansheng] Shantou Univ Med Coll, Affiliated Hosp 1, Dept Thorac Surg, Shantou, Peoples R China; [Chen, Rixin] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Res Ctr Med Sci, Guangzhou, Peoples R China; [Lu, Guojie] Guangzhou Panyu Cent Hosp, Dept Thorac Surg, Guangzhou, Peoples R China; [Mendogni, Paolo] Fdn IRCCS Ca Granda Osped Maggiore Policlin, Thorac Surg & Lung Transplant Unit, Milan, Italy; [Migliore, Marcello] Univ Hosp Wales, Cardiothorac Dept, Thorac Surg, Cardiff, Wales; [Migliore, Marcello] Univ Catania, Univ Hosp Catania, Dept Surg & Med Specialties, Minimally Invas Surg & New Technol, Catania, Italy; [Kang, Min-Woong] Chungnam Natl Univ, Sch Med, Dept Thorac & Cardiovasc Surg, Daejeon, South Korea; [Kanzaki, Ryu] Osaka Univ, Grad Sch Med, Dept Gen Thorac Surg, Osaka, Japan; [Yang, Jiancheng] Ecole Polytech Fed Lausanne EPFL, Comp Vis Lab CVLab, Lausanne, Switzerland"

通信作者："Qiao, GB (通讯作者)，Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Thorac Surg, 106 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China.; Qiao, GB (通讯作者)，Southern Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China.; Shi, QL (通讯作者)，Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, 1 Med Coll Rd, Chongqing 400016, Peoples R China."

来源：EUROPEAN RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000895582800001

JCR分区：Q1

影响因子：4.7

年份：2023

卷号：　

期号：　

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结束页：　

文献类型：Article; Early Access

关键词：Lung neoplasms; Nomograms; Autoantibodies; Artificial intelligence

摘要："ObjectiveTo construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure. MethodsThis study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set. ResultsA diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851-0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of & LE; 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022). ConclusionsBased on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with & LE; 2 cm diameters."

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