Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Nutrition, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
J Surg Oncol. 2022 Dec;126(7):1316-1329. doi: 10.1002/jso.27041. Epub 2022 Aug 17.
The main purpose of this study was to develop and validate a clinical model for estimating the risk of malignancy in solitary pulmonary nodules (SPNs).
A total of 672 patients with SPNs were retrospectively reviewed. The least absolute shrinkage and selection operator algorithm was applied for variable selection. A regression model was then constructed with the identified predictors. The discrimination, calibration, and clinical validity of the model were evaluated by the area under the receiver-operating-characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).
Ten predictors, including gender, age, nodule type, diameter, lobulation sign, calcification, vascular convergence sign, mediastinal lymphadenectasis, the natural logarithm of carcinoembryonic antigen, and combination of cytokeratin 19 fragment 21-1, were incorporated into the model. The prediction model demonstrated valuable prediction performance with an AUC of 0.836 (95% CI: 0.777-0.896), outperforming the Mayo (0.747, p = 0.024) and PKUPH (0.749, p = 0.018) models. The model was well-calibrated according to the calibration curves. The DCA indicated the nomogram was clinically useful over a wide range of threshold probabilities.
This study proposed a clinical model for estimating the risk of malignancy in SPNs, which may assist clinicians in identifying the pulmonary nodules that require invasive procedures and avoid the occurrence of overtreatment.
本研究的主要目的是开发和验证一种用于评估孤立性肺结节（SPN）恶性肿瘤风险的临床模型。
回顾性分析了 672 例 SPN 患者。应用最小绝对收缩和选择算子算法进行变量选择。然后，用确定的预测因子构建回归模型。通过受试者工作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）评估模型的区分度、校准度和临床有效性。
纳入了 10 个预测因子，包括性别、年龄、结节类型、直径、分叶征、钙化、血管汇聚征、纵隔淋巴结肿大、癌胚抗原的自然对数和细胞角蛋白 19 片段 21-1 的组合。预测模型具有较高的预测性能，AUC 为 0.836（95%CI：0.777-0.896），优于 Mayo（0.747，p=0.024）和 PKUPH（0.749，p=0.018）模型。校准曲线显示模型具有良好的校准度。DCA 表明，在广泛的阈值概率范围内，列线图具有临床应用价值。
本研究提出了一种用于评估 SPN 恶性肿瘤风险的临床模型，该模型可能有助于临床医生识别需要进行有创检查的肺部结节，避免过度治疗的发生。
