CT影像学特征、血清肺癌自身抗体的肺炎型肺癌诊断模型构建
张孝飞 , 张影 , 王迎利 , 王少强 , 杨瑞祥 , 钱会 , 蒋亚林
中国现代医学杂志 ›› 2025, Vol. 35 ›› Issue (11) : 13 -21.
CT影像学特征、血清肺癌自身抗体的肺炎型肺癌诊断模型构建
Construction of a diagnostic model for pneumonic-type lung cancer based on CT imaging features and seven serum tumor-associated autoantibodies
Objective To construct a diagnostic model for pneumonic-type lung cancer (PTLC) based on CT imaging features and seven serum tumor-associated autoantibodies (TAAbs). Methods This retrospective study included 224 suspected lung cancer patients (February 2021 to August 2024), divided into modeling (n = 184) and internal validation (n = 40) cohorts. An external validation cohort (n = 40) was independently enrolled. PTLC (n = 120) and lobar pneumonia (n = 64) groups were compared for CT features (mediastinal lymphadenopathy, air bronchogram, spiculation, vacuolar sign) and TAAbs. Multivariate logistic regression and ROC analysis were performed. Results Multivariate general logistic regression analysis showed that enlarged mediastinal lymph node[O^R = 1.550 (95% CI: 1.298, 1.851) ], air bronchogram sign [O^R = 7.147 (95% CI: 1.641, 31.129) ], spicule sign [O^R = 7.486 (95% CI: 2.182, 25.681) ], vacuolar sign [O^R = 13.077 (95% CI: 2.912, 58.715) ] and positivity of 7 TAAbs [O^R = 6.746 (95% CI: 1.933, 23.549) ] were risk factors for diagnosing pneumonic-type lung cancer (P < 0.05). The diagnostic model was Logit (P) = -7.691 + 0.438 × enlarged mediastinal lymph node + 1.967 × air bronchogram sign + 2.013 × spicule sign + 2.571 × vacuolar sign + 1.909 × positivity of 7 TAAbs. ROC curves indicated that the areas under the curve (AUCs) of the model in the modeling group, the internal validation group and the external validation group were 0.943 (95% CI: 0.899, 0.972), 0.870(95% CI: 0.752, 0.989), and 0.842 (95% CI: 0.710, 0.974), respectively. The sensitivities were 94.2% (95% CI: 0.884, 0.976), 92.3% (95% CI: 0.640, 0. 998), and 84.62% (95% CI: 0.546, 0.981). The specificities were 84.4% (95% CI: 0.731, 0.922), 81.5% (95% CI: 0.619, 0.937), and 81.5% (95% CI: 0.619, 0.937). Homser-Lemeshow goodness of fit test test showed good fit (P > 0.05). Conclusion CT images of lung cancer patients have typical features, and the positive rates of 7 TAAbs are relatively high. The diagnostic model constructed based on CT image features and 7 TAAbs in serum may be used in clinical auxiliary diagnosis of pneumonic-type lung cancer.
肺炎型肺癌 / 大叶性肺炎 / CT影像特征 / 肺癌自身抗体 / 诊断模型
pneumonic-type lung cancer / lobar pneumonia / CT image features / tumor-associated autoantibodies / diagnostic model
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安徽省卫生健康科研项目(AHWJ2023A20189)
亳州市卫健委科研项目(bzwj2022b008)
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