Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
National Center for Drug Research and Evaluation, National Institute of Health (ISS), Rome, Italy.
J Endocrinol Invest. 2024 Oct;47(10):2575-2586. doi: 10.1007/s40618-024-02346-x. Epub 2024 Mar 23.
Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.
We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.
By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.
A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.
肺神经内分泌肿瘤（NET）可分为典型类癌（TC）和非典型类癌（AC），其生物学和预后具有高度异质性。组织学亚型和 TNM 分期是肺 NET 的明确预后因素。在我们之前的一项研究中，我们证明了肿瘤侧别的对肺 NET 生存结局的显著影响。
我们开发了一个列线图模型，该模型整合了相关的预后因素来预测肺 NET 的结局。通过为模型中包含的每个变量的得分相加，我们可以获得一个预后评分（Rachel 评分）。应用 Wilcoxon 非参数统计检验比较参数之间的差异，并使用 Harrell 的一致性指数来衡量模型的预测能力。为了测试模型的判别能力和预测准确性，我们计算了 Gonen 和 Heller 一致性指数。时间依赖性 ROC 曲线及其曲线下面积（AUC）用于评估模型的预测性能。
通过应用 Rachel 评分，我们能够识别出三个预后组（高、中、低风险）。这三组与根据获得的列线图定义的明确分数范围相关（I：0-90，II：91-130；III：>130 分），为预后分层提供了有用的工具。Rachel 评分识别的三组之间的总生存（OS）和无进展生存（PFS）Kaplan-Meier 曲线证实存在显著差异（p < 0.0001）。
本研究建立了一个预后列线图，纳入了对肺 NET 生存有显著影响的变量。该列线图在预测该人群的 OS 和 PFS 方面表现出令人满意且稳定的能力，证实了除 TC 与 AC 组织学诊断之外的异质性。
