Department of Radiation Oncology, City of Hope National Medical Center, Duarte, United States.
Department of Surgery, City of Hope National Medical Center, Duarte, United States.
Radiother Oncol. 2022 Aug;173:10-18. doi: 10.1016/j.radonc.2022.05.019. Epub 2022 May 23.
The role of post-operative radiotherapy (PORT) for completely resected N2 non-small-cell lung cancer (NSCLC) is controversial in light of recent randomized data. We sought to utilize machine learning to identify a subset of patients who may still benefit from PORT based on extent of nodal involvement.
MATERIALS/METHODS: Patients with completely resected N2 NSCLC were identified in the National Cancer Database. We trained a machine-learning based model of overall survival (OS). SHapley Additive exPlanation (SHAP) values were used to identify prognostic and predictive thresholds of number of positive lymph nodes (LNs) involved and lymph node ratio (LNR). Cox proportional hazards regression was used for confirmatory analysis.
A total of 16,789 patients with completely resected N2 NSCLC were identified. Using the SHAP values, we identified thresholds of 3+ positive LNs and a LNR of 0.34+. On multivariate analysis, PORT was not significantly associated with OS (p = 0.111). However, on subset analysis of patients with 3+ positive LNs, PORT improved OS (HR: 0.91; 95% CI: 0.86-0.97; p = 0.002). On a separate subset analysis in patients with a LNR of 0.34+, PORT improved OS (HR: 0.90; 95% CI: 0.85-0.96; p = 0.001). Patients with 3+ positive lymph nodes had a 5-year OS of 38% with PORT compared to 31% without PORT. Patient with positive lymph node ratio 0.34+ had a 5-year OS of 38% with PORT compared to 29% without PORT.
Patients with a high lymph node burden or lymph node ratio may present a subpopulation of patients who could benefit from PORT. To our knowledge, this is the first study to use machine learning algorithms to address this question with a large national dataset. These findings address an important question in the field of thoracic oncology and warrant further investigation in prospective studies.
鉴于最近的随机数据，对于完全切除的 N2 非小细胞肺癌（NSCLC）患者，术后放疗（PORT）的作用仍存在争议。我们试图利用机器学习根据淋巴结受累程度确定仍可能从 PORT 中获益的患者亚组。
材料/方法：在国家癌症数据库中确定了完全切除的 N2 NSCLC 患者。我们训练了一个基于整体生存（OS）的机器学习模型。使用 SHapley Additive exPlanation（SHAP）值来确定阳性淋巴结（LNs）数量和淋巴结比例（LNR）的预后和预测阈值。Cox 比例风险回归用于确认分析。
共确定了 16789 例完全切除的 N2 NSCLC 患者。使用 SHAP 值，我们确定了 3+阳性 LNs 和 LNR 为 0.34+的阈值。多变量分析显示，PORT 与 OS 无显著相关性（p=0.111）。然而，在 3+阳性 LNs 患者的亚组分析中，PORT 改善了 OS（HR：0.91；95%CI：0.86-0.97；p=0.002）。在 LNR 为 0.34+的另一亚组分析中，PORT 改善了 OS（HR：0.90；95%CI：0.85-0.96；p=0.001）。3+阳性淋巴结患者 PORT 组的 5 年 OS 为 38%，无 PORT 组为 31%。阳性淋巴结比率为 0.34+的患者 PORT 组的 5 年 OS 为 38%，无 PORT 组为 29%。
淋巴结负担或淋巴结比率高的患者可能存在受益于 PORT 的亚组患者。据我们所知，这是第一项使用机器学习算法利用大型国家数据集解决此问题的研究。这些发现解决了胸部肿瘤学领域的一个重要问题，需要在前瞻性研究中进一步探讨。
