Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China.
Laboratory of Respiratory Disease and Thoracic Surgery, KU Leuven, 3000, Leuven, Belgium.
BMC Pulm Med. 2023 Jan 30;23(1):44. doi: 10.1186/s12890-023-02341-7.
Accurately evaluating the lymph node status preoperatively is critical in determining the appropriate treatment plan for non-small-cell lung cancer (NSCLC) patients. This study aimed to construct a novel nomogram to predict the probability of lymph node metastasis in clinical T1 stage patients based on non-invasive and easily accessible indicators.
From October 2019 to June 2022, the data of 84 consecutive cT1 NSCLC patients who had undergone PET/CT examination within 30 days before surgery were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify the risk factors of lymph node metastasis. A nomogram based on these predictors was constructed. The area under the receiver operating characteristic (ROC) curve and the calibration curve was used for assessment. Besides, the model was confirmed by bootstrap resampling.
Four predictors (tumor SUVmax value, lymph node SUVmax value, consolidation tumor ratio and platelet to lymphocyte ratio) were identified and entered into the nomogram. The model indicated certain discrimination, with an area under ROC curve of 0.921(95%CI 0.866-0.977). The calibration curve showed good concordance between the predicted and actual possibility of lymph node metastasis.
This nomogram was practical and effective in predicting lymph node metastasis for patients with cT1 NSCLC. It could provide treatment recommendations to clinicians.
准确评估非小细胞肺癌（NSCLC）患者术前的淋巴结状态对于确定合适的治疗方案至关重要。本研究旨在构建一种新的列线图，基于无创且易于获取的指标预测临床 T1 期患者发生淋巴结转移的概率。
回顾性收集了 2019 年 10 月至 2022 年 6 月间 84 例连续 cT1 NSCLC 患者的资料，这些患者在术前 30 天内接受了 PET/CT 检查。通过单因素和多因素逻辑回归分析确定淋巴结转移的风险因素。基于这些预测因素构建了一个列线图。使用受试者工作特征（ROC）曲线下面积和校准曲线进行评估。此外，还通过自举重采样法对模型进行了验证。
确定了 4 个预测因素（肿瘤 SUVmax 值、淋巴结 SUVmax 值、实变肿瘤比例和血小板与淋巴细胞比值），并将其纳入列线图。该模型具有一定的判别能力，ROC 曲线下面积为 0.921（95%CI 0.866-0.977）。校准曲线显示预测和实际淋巴结转移可能性之间具有良好的一致性。
该列线图可有效预测 cT1 NSCLC 患者的淋巴结转移情况，可为临床医生提供治疗建议。
