Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
JCO Clin Cancer Inform. 2021 Dec;5:1220-1231. doi: 10.1200/CCI.21.00001.
The American Joint Committee on Cancer (AJCC) eighth edition schema for pancreatic ductal adenocarcinoma treats T and N stage as independent factors and uses positive lymph nodes (PLNs) to define N stage, despite data favoring lymph node ratio (LNR). We used artificial intelligence-based techniques to compare PLN with LNR and investigate interactions between tumor size and nodal status.
Patients who underwent pancreatic ductal adenocarcinoma resection between 2000 and 2017 at six institutions were identified. LNR and PLN were compared through shapley additive explanations (SHAP) analysis, with the best predictor used to define nodal status. We trained optimal classification trees (OCTs) to predict 1-year and 3-year risk of death, incorporating only tumor size and nodal status as variables. The OCTs were compared with the AJCC schema and similarly trained XGBoost models. Variable interactions were explored via SHAP.
Two thousand eight hundred seventy-four patients comprised the derivation and 1,231 the validation cohort. SHAP identified LNR as a superior predictor. The OCTs outperformed the AJCC schema in the derivation and validation cohorts (1-year area under the curve: 0.681 0.603; 0.638 0.586, 3-year area under the curve: 0.682 0.639; 0.675 0.647, respectively) and performed comparably with the XGBoost models. We identified interactions between LNR and tumor size, suggesting that a negative prognostic factor partially overrides the effect of a concurrent favorable factor.
Our findings highlight the superiority of LNR and the importance of interactions between tumor size and nodal status. These results and the potential of the OCT methodology to combine them into a powerful, visually interpretable model can help inform future staging systems.
美国癌症联合委员会（AJCC）第八版胰腺癌分期方案将 T 期和 N 期视为独立因素，并使用阳性淋巴结（PLN）定义 N 期，尽管有数据支持淋巴结比率（LNR）。我们使用基于人工智能的技术来比较 PLN 与 LNR，并研究肿瘤大小和淋巴结状态之间的相互作用。
在六个机构中，我们确定了 2000 年至 2017 年间接受胰腺导管腺癌切除术的患者。通过 SHAP 解释（SHAP）分析比较 LNR 和 PLN，使用最佳预测因子来定义淋巴结状态。我们使用仅包含肿瘤大小和淋巴结状态作为变量的最优分类树（OCT）来预测 1 年和 3 年死亡风险。OCT 与 AJCC 分期和类似训练的 XGBoost 模型进行比较。通过 SHAP 探索变量相互作用。
2874 例患者为推导队列，1231 例患者为验证队列。SHAP 确定 LNR 是一个更好的预测因子。OCT 在推导和验证队列中的表现优于 AJCC 分期（1 年 AUC：0.681 0.603；0.638 0.586，3 年 AUC：0.682 0.639；0.675 0.647），与 XGBoost 模型表现相当。我们发现 LNR 和肿瘤大小之间存在相互作用，表明负预后因素部分抵消了同时存在的有利因素的影响。
我们的研究结果强调了 LNR 的优越性以及肿瘤大小和淋巴结状态之间相互作用的重要性。这些结果以及 OCT 方法学将它们结合到一个强大、视觉可解释的模型中的潜力，可以帮助为未来的分期系统提供信息。
