Zhao Wei-Hua, Zhang Li-Juan, Li Xian, Luo Tian-You, Lv Fa-Jin, Li Qi
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Pathology, Chongqing Medical University, Chongqing, China.
Acad Radiol. 2025 Feb;32(2):1067-1077. doi: 10.1016/j.acra.2024.09.016. Epub 2024 Sep 21.
To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).
The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared. Binary logistic regression analysis was performed to identify the independent factors for diagnosing AI-SPNs. An external validation cohort included 133 consecutive patients to test the model's predictive efficiency.
Univariate analysis showed that age < 62 years, male sex, maximum spiculation length > 9 mm, polygonal shapes, three-planar ratio > 1.48, Lung window/mediastinal window (L/M) ratio > 1.13, pleural tag type I, satellite lesions, and halo sign were more frequent in AI-SPNs, whereas pleural tag type III, bronchial truncation, and perifocal fibrosis were more common in malignant SPNs (M-SPNs) (all P < 0.05). Binary logistic regression showed age < 62 years, male sex, polygonal shape, three-planar ratio > 1.48, L/M ratio > 1.13, pleural tag type I, satellite lesions, halo sign, and absence of bronchial truncation were independent factors for diagnosing AI-SPNs (AUC, sensitivity, specificity, and accuracy of 0.951, 83.30%, 92.30%, and 87.20%, respectively). In the external validation cohort, the AUC, sensitivity, specificity, and accuracy were 0.969, 90.47%, 90.00%, and 90.23%, respectively.
AI-SPNs and M-SPNs exhibited different clinical and imaging characteristics. A good understanding of these differences may help reduce diagnostic errors in AI-SPNs and enable to choose an optimal treatment strategy.
探讨形态学提示恶性的炎性实性肺结节（SPN），以下简称非典型炎性SPN（AI-SPN）的临床及计算机断层扫描特征。
回顾性分析515例行手术切除的SPN患者的CT数据。将这些患者分为炎性组和恶性组，比较其临床及影像特征。进行二元逻辑回归分析以确定诊断AI-SPN的独立因素。一个外部验证队列包括133例连续患者，以测试模型的预测效率。
单因素分析显示，AI-SPN中年龄<62岁、男性、最大毛刺长度>9mm、多边形形状、三维比率>1.48、肺窗/纵隔窗（L/M）比率>1.13、I型胸膜皱襞、卫星灶和晕征更为常见，而III型胸膜皱襞、支气管截断和灶周纤维化在恶性SPN（M-SPN）中更为常见（所有P<0.05）。二元逻辑回归显示年龄<62岁、男性、多边形形状、三维比率>1.48、L/M比率>1.13、I型胸膜皱襞、卫星灶、晕征和无支气管截断是诊断AI-SPN的独立因素（AUC、敏感性、特异性和准确性分别为0.951、83.30%、92.30%和87.20%）。在外部验证队列中，AUC、敏感性、特异性和准确性分别为0.969、90.47%、90.00%和90.23%。
AI-SPN和M-SPN表现出不同的临床及影像特征。充分了解这些差异可能有助于减少AI-SPN的诊断错误，并有助于选择最佳治疗策略。
