Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, 200433, P. R. China.
Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510000, P. R. China.
Respir Res. 2023 Jun 23;24(1):168. doi: 10.1186/s12931-023-02440-3.
BACKGROUND: The current nodal (pN) classification still has limitations in stratifying the prognosis of small cell lung cancer (SCLC) patients with pathological classifications T1-2N0-2M0. Thus. This study aimed to develop and validate a modified nodal classification based on a multicenter cohort. MATERIALS AND METHODS: We collected 1156 SCLC patients with pathological classifications T1-2N0-2M0 from the Surveillance, Epidemiology, and End Results database and a multicenter database in China. The X-tile software was conducted to determine the optimal cutoff points of the number of examined lymph nodes (ELNs) and lymph node ratio (LNR). The Kaplan-Meier method, the Log-rank test, and the Cox regression method were used in this study. We classified patients into three pathological N modification categories, new pN#1 (pN0-#ELNs > 3), new pN#2 (pN0-#ELNs ≤ 3 or pN1-2-#LNR ≤ 0.14), and new pN#3 (N1-2-#LNR > 0.14). The Akaike information criterion (AIC), Bayesian Information Criterion, and Concordance index (C-index) were used to compare the prognostic, predictive ability between the current pN classification and the new pN component. RESULTS: The new pN classification had a satisfactory effect on survival curves (Log-rank P < 0.001). After adjusting for other confounders, the new pN classification could be an independent prognostic indicator. Besides, the new pN component had a much more accurate predictive ability in the prognostic assessment for SCLC patients of pathological classifications T1-2N0-2M0 compared with the current pN classification in the SEER database (AIC: 4705.544 vs. 4731.775; C-index: 0.654 vs. 0.617, P < 0.001). Those results were validated in the MCDB from China. CONCLUSIONS: The multicenter cohort developed and validated a modified nodal classification for SCLC patients with pathological category T1-2N0-2M0 after surgery. Besides, we propose that an adequate lymph node dissection is essential; surgeons should perform and consider the situation of ELNs and LNR when they evaluate postoperative prognoses of SCLC patients.
背景：目前的淋巴结（pN）分类在分层小细胞肺癌（SCLC）患者的病理分类 T1-2N0-2M0 方面仍存在局限性。因此，本研究旨在基于多中心队列开发和验证改良的淋巴结分类。
材料和方法：我们从监测、流行病学和结果数据库（SEER）和中国的多中心数据库中收集了 1156 名病理分类为 T1-2N0-2M0 的 SCLC 患者。使用 X-tile 软件确定检查淋巴结（ELNs）数量和淋巴结比率（LNR）的最佳截断值。本研究采用 Kaplan-Meier 法、Log-rank 检验和 Cox 回归法。我们将患者分为三种病理 N 修正类别，新 pN#1（pN0-#ELNs＞3）、新 pN#2（pN0-#ELNs≤3 或 pN1-2-#LNR≤0.14）和新 pN#3（N1-2-#LNR＞0.14）。使用赤池信息量准则（AIC）、贝叶斯信息准则和一致性指数（C-index）比较当前 pN 分类与新 pN 成分在预后、预测能力方面的差异。
结果：新的 pN 分类对生存曲线有较好的效果（Log-rank P＜0.001）。在调整其他混杂因素后，新的 pN 分类可以作为独立的预后指标。此外，新的 pN 成分在预测 SEER 数据库中病理分类 T1-2N0-2M0 的 SCLC 患者预后方面具有更高的准确性（AIC：4705.544 与 4731.775；C-index：0.654 与 0.617，P＜0.001）。这些结果在中国的 MCDB 中得到了验证。
结论：本研究开发和验证了一种改良的淋巴结分类，适用于术后病理分类为 T1-2N0-2M0 的 SCLC 患者。此外，我们建议进行充分的淋巴结清扫；外科医生在评估 SCLC 患者的术后预后时，应考虑到 ELNs 和 LNR 的情况。
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