Wang Ruoxuan, Qi Tianjie
Master Student, No. 215, Heping West Road, The Second Hospital of Hebei Medical University, Xinhua District, Hebei Province, China.
Chief Physician, No.215 Heping West Road, Second Hospital of Hebei Medical University, Xinhua District, Hebei Province China.
Curr Probl Diagn Radiol. 2025 Jul-Aug;54(4):443-448. doi: 10.1067/j.cpradiol.2024.12.014. Epub 2024 Dec 31.
Distinguishing between benign and malignant pulmonary nodules based on CT imaging features such as the spiculation sign and/or lobulation sign remains challenging and these nodules are often misinterpreted as malignant tumors. this retrospective study aimed to develop a prediction model to estimate the likelihood of benign and malignant lung nodules exhibiting spiculation and/or lobulation signs.
A total of 500 patients with pulmonary nodules from June 2022 to August 2024 were retrospectively analyzed. Among them, 190 patients with spiculation sign and lobar sign or both on CT scan were included in this study. This investigation collected the clinical information, preoperative chest CT imaging characteristics, and postoperative histopathologic results from patients.Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model performance was assessed through receiver operating characteristic(ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).
In our study, 190 patients with pulmonary nodules underwent lung biopsy in 10 patients and surgical resection in 180 patients, of whom 53 were benign nodules and 137 were malignant nodules. When combined with the spiculation sign or (and) the lobulation sign, the vascular cluster sign, bronchial architectural distortion, bubble-like translucent area, nodule density, and CEA were found to be significant independent predictors for determining the benignity and malignancy of pulmonary nodules. The nomogram prediction model demonstrated high predictive accuracy with an area under the ROC curve (AUC) of 0.904. Furthermore, the model's calibration curve demonstrated adequate calibration. DCA confirmed the prediction model's validity.
The model can assist clinicians in making more accurate preoperative diagnoses and in guiding clinical decision-making regarding treatment, potentially reducing unnecessary surgical interventions.
基于毛刺征和/或分叶征等CT影像特征来区分肺结节的良恶性仍然具有挑战性，这些结节常被误诊为恶性肿瘤。本回顾性研究旨在开发一种预测模型，以估计表现出毛刺征和/或分叶征的肺结节为良性和恶性的可能性。
回顾性分析了2022年6月至2024年8月期间共500例肺结节患者。其中，190例在CT扫描上有毛刺征和叶征或两者皆有的患者被纳入本研究。本调查收集了患者的临床信息、术前胸部CT影像特征和术后组织病理学结果。采用单因素和多因素逻辑回归分析来识别独立危险因素，据此建立了预测模型和列线图。此外，通过受试者操作特征（ROC）曲线分析、校准曲线分析和决策曲线分析（DCA）对模型性能进行评估。
在我们的研究中，190例肺结节患者中，10例行肺活检，180例行手术切除，其中53例为良性结节，137例为恶性结节。当与毛刺征或（和）分叶征相结合时，发现血管集束征、支气管结构扭曲、泡状透光区、结节密度和癌胚抗原是确定肺结节良恶性的重要独立预测因素。列线图预测模型显示出较高的预测准确性，ROC曲线下面积（AUC）为0.904。此外，该模型的校准曲线显示校准良好。DCA证实了预测模型的有效性。
该模型可协助临床医生进行更准确的术前诊断，并指导治疗方面的临床决策，可能减少不必要的手术干预。
