Department of Radiology, The First Medical Center of the Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China.
Nankai University, Tianjin, China.
Sci Rep. 2024 Aug 5;14(1):18085. doi: 10.1038/s41598-024-69138-4.
The objective of this study was to develop a nomogram model based on the natural progression of tumor and other radiological features to discriminate between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. A retrospective analysis was conducted on 15,655 cases of lung adenocarcinoma diagnosed at our institution between January 2010 and June 2023. Primary nodular invasive mucinous adenocarcinomas and non-mucinous adenocarcinomas with at least two preoperative CT scans were included. These patients were randomly assigned to training and validation sets. Univariate and multivariate analyses were employed to compare tumor growth rates and clinical radiological characteristics between the two groups in the training set. A nomogram model was constructed based on the results of multivariate analysis. The diagnostic value of the model was evaluated in both the training and validation sets using calibration curves and receiver operating characteristic curves (ROC). The study included 174 patients, with 58 cases of mucinous adenocarcinoma and 116 cases of non-mucinous adenocarcinoma. The nomogram model incorporated the maximum tumor diameter, the consolidation/tumor ratio (CTR), and the specific growth rate (SGR) to generate individual scores for each patient, which were then accumulated to obtain a total score indicative of the likelihood of developing mucinous or non-mucinous adenocarcinoma. The model demonstrated excellent discriminative ability with an area under the receiver operating characteristic curve of 0.784 for the training set and 0.833 for the testing set. The nomogram model developed in this study, integrating SGR with other radiological and clinical parameters, provides a valuable and accurate tool for differentiating between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. This prognostic model offers a robust and objective basis for personalized management of patients with pulmonary adenocarcinomas.
本研究旨在基于肿瘤的自然进展和其他影像学特征，建立一个列线图模型，以区分孤立性结节状肺黏液性腺癌和非黏液性腺癌。我们对 2010 年 1 月至 2023 年 6 月在我院诊断的 15655 例肺腺癌病例进行了回顾性分析。纳入了原发性结节性浸润性黏液腺癌和至少有两次术前 CT 扫描的非黏液性腺癌患者。这些患者被随机分配到训练集和验证集中。在训练集中，我们采用单因素和多因素分析比较了两组患者的肿瘤生长率和临床影像学特征。根据多因素分析的结果，构建了一个列线图模型。我们使用校准曲线和受试者工作特征曲线（ROC）在训练集和验证集中评估了该模型的诊断价值。本研究共纳入 174 例患者，其中黏液腺癌 58 例，非黏液腺癌 116 例。该列线图模型纳入了最大肿瘤直径、实变/肿瘤比值（CTR）和特定增长率（SGR），为每位患者生成个体得分，然后累加这些得分以获得提示发生黏液性或非黏液性腺癌可能性的总得分。该模型在训练集和验证集的受试者工作特征曲线下面积分别为 0.784 和 0.833，具有良好的鉴别能力。本研究中建立的列线图模型，将 SGR 与其他影像学和临床参数相结合，为区分孤立性结节状肺黏液性腺癌和非黏液性腺癌提供了一种有价值且准确的工具。该预后模型为肺腺癌患者的个体化管理提供了一个稳健和客观的基础。
