Background

Neoadjuvant therapy is associated with nodal downstaging and improved oncological outcomes in patients with lymph node (LN)-positive pancreatic cancer. This study aimed to develop and validate a nomogram to preoperatively predict LN-positive disease.

Methods

A total of 558 patients with resected pancreatic cancer were randomly and equally divided into development and internal validation cohorts. Multivariate logistic regression analysis was used to construct the nomogram. Model performance was evaluated by discrimination, calibration, and clinical usefulness. An independent multicenter cohort consisting of 250 patients was used for external validation.

Results

A four-marker signature was built consisting of carbohydrate antigen 19–9 (CA19–9), CA125, CA50, and CA242. A nomogram was constructed to predict LN metastasis using three predictors identified by multivariate analysis: risk score of the four-marker signature, computed tomography-reported LN status, and clinical tumor stage. The prediction model exhibited good discrimination ability, with C-indexes of 0.806, 0.742 and 0.763 for the development, internal validation, and external validation cohorts, respectively. The model also showed good calibration and clinical usefulness. A cut-off value (0.72) for the probability of LN metastasis was determined to separate low-risk and high-risk patients. Kaplan-Meier survival analysis revealed a good agreement of the survival curves between the nomogram-predicted status and the true LN status.

Conclusions

This nomogram enables the identification of pancreatic cancer patients at high risk for LN positivity who may have more advanced disease and thus could potentially benefit from neoadjuvant therapy.

中文翻译：

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用于术前预测胰腺癌淋巴结阳性的列线图 (NeoPangram) 的开发和多中心验证

背景

新辅助治疗与淋巴结（LN）阳性胰腺癌患者的淋巴结降期和改善肿瘤学结果有关。本研究旨在开发和验证列线图以在术前预测 LN 阳性疾病。

方法

共有 558 名切除胰腺癌的患者被随机平均分为开发组和内部验证组。多变量逻辑回归分析用于构建列线图。通过辨别、校准和临床实用性评估模型性能。由 250 名患者组成的独立多中心队列用于外部验证。

结果

构建了一个由碳水化合物抗原 19-9 (CA19-9)、CA125、CA50 和 CA242 组成的四标记特征。使用通过多变量分析确定的三个预测因子构建列线图来预测 LN 转移：四标志物特征的风险评分、计算机断层扫描报告的 LN 状态和临床肿瘤分期。预测模型表现出良好的辨别能力，开发组、内部验证组和外部验证组的 C 指数分别为 0.806、0.742 和 0.763。该模型还显示出良好的校准和临床实用性。确定 LN 转移概率的临界值 (0.72) 以区分低风险和高风险患者。Kaplan-Meier 生存分析揭示了列线图预测状态和真实 LN 状态之间的生存曲线的良好一致性。

结论

该列线图能够识别出 LN 阳性高风险的胰腺癌患者，这些患者可能患有更晚期的疾病，因此可能从新辅助治疗中受益。