He Xiao-Qun, Huang Xing-Tao, Luo Tian-You, Liu Xiao, Li Qi
Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1348-1358. doi: 10.21037/qims-23-995. Epub 2024 Jan 2.
Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes.
From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs.
In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001).
CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.
计算机断层扫描（CT）一直是诊断实性孤立性肺结节（SSPNs）的首选方法。然而，SSPNs越小，其良恶性之间的CT鉴别征象就越少，这给它们的诊断带来了巨大挑战。因此，本研究旨在探讨不同大小的小（≤15mm）良性和恶性SSPNs之间的CT鉴别特征。
回顾性分析2018年5月至2021年11月794例小SSPNs（≤15mm）患者的CT数据。将SSPNs分为良性和恶性组，每组再进一步分为三个队列：队列I（直径≤6mm）、队列II（6mm＜直径≤8mm）和队列III（8mm＜直径≤15mm）。确定三个队列中良性和恶性SSPNs的CT鉴别特征。进行多变量逻辑回归分析以确定良性SSPNs的独立因素。
在队列I中，组间多边形形态和上叶分布差异有统计学意义（均P＜0.05），多参数分析显示多边形形态[调整优势比（OR）：12.165；95%置信区间（CI）：1.512 - 97.872；P = 0.019]是预测良性SSPNs最有效的变量，受试者操作特征曲线（AUC）下面积为0.747（95%CI：0.640 - 0.855；P = 0.001）。在队列II中，组间多边形形态、分叶、胸膜凹陷和气支气管征差异有统计学意义（均P＜0.05），多边形形态（OR：8.870；95%CI：1.096 - 71.772；P = 0.041）和无胸膜凹陷（OR：0.306；95%CI：0.106 - 0.883；P = 0.028）是良性SSPNs的独立预测因素，AUC为0.778（95%CI：0.694 - 0.863；P＜0.001）。在队列III中，12个CT特征在组间差异有统计学意义（均P＜0.05），多边形形态（OR：3.953；95%CI：1.508 - 10.361；P = 0.005）；钙化（OR：3.710；95%CI：1.305 - 10.551；P = 0.014）；晕征（OR：6.237；95%CI：2.838 - 13.710；P＜0.001）；卫星灶（OR：6.554；95%CI：3.225 - 13.318；P＜0.001）；无分叶（OR：0.066；95%CI：0.026 - 0.167；P＜0.001）、空气潴留（OR：0.405；95%CI：0.215 - 0.764；P = 0.005）、胸膜凹陷（OR：0.297；95%CI：0.179 - 0.493；P＜0.001）、支气管截断（OR：0.165；95%CI：0.090 - 0.303；P＜0.001）和气支气管征（OR：0.363；95%CI：0.208 - 0.633；P＜0.001）是良性SSPNs的独立预测因素，AUC为0.869（95%CI：0.840 - 0.897；P＜0.001）。
不同大小的SSPNs的CT特征有所不同。明确基于不同直径范围的CT鉴别特征可能有助于减少模糊性，并将良性SSPNs与恶性SSPNs区分开来。
