Department of Pediatrics, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
Department of Laboratory Medicine, Sun Yat-Sen University Cancer Center, Sun Yat-Sen University, Guangzhou, 510060, Guangdong, China.
BMC Pediatr. 2023 Aug 26;23(1):426. doi: 10.1186/s12887-023-04228-2.
Despite multiple attempts have been made to develop risk stratification within high-risk neuroblastoma (NB) patients (age of diagnosis ≥ 18 month-old with metastatic NB), the definition of "ultra high-risk NB" is still lack of consensus, and indicators for identifying this subgroup are still unclear. This study aimed to develop a nomogram based on easy-to-obtain blood-derived biofactors for identifying ultra high-risk NB patients with highest risk of death within 3 or 5 years.
One hundred sixty-seven NB patients who treated at Sun Yat-sen University Cancer Center between 2015 and 2023 were recruited and clustered randomly into training and validation cohorts (116 and 51 cases, respectively). Univariate and multivariate Cox analysis were performed in training set to screen independent prognostic indicators for constructing nomogram model of predicting 1-, 3- and 5-year overall survival (OS). The discrimination power of the nomogram in training and validation sets were assessed by concordance index (C-index) and calibration plot. Based on the risk score obtained from nomogram model, the prognostic accuracy of 1-, 3- and 5-year OS rates in training and validation cohorts were further evaluated using the area under receiver operating characteristic (ROC) curves (AUC).
Through univariate and multivariate Cox analysis, independent prognostic indicators, including serum lactate dehydrogenase (LDH) and albumin (ALB), were identified in training set, and used to establish a nomogram model. The model showed good discrimination power with C-index in training cohort being 0.706 (95%CI: 0.633-0.788). According to the cut-point calculated based on the established nomogram, patients with a nomogram score > 34 points could be stratified to ultra high-risk NB subgroup, and this subgroup had poorer OS than those in non-ultra one (p < 0.001). AUC values of ROC curves for 3- and 5-year OS rates in the training set were 0.758 and 0.756, respectively. Moreover, based on the cut-point score (34 points) developed in training set, The model also showed good discrimination power with C-index of 0.773 (95%CI: 0.664-0.897) and powerful prognostic accuracy of AUC for 3- and 5-year OS rates being 0.825 and 0.826, respectively, in validation cohort.
We developed a simple-to-use nomogram based on common laboratory indicators to identify the subgroup of ultra high-risk NB before treatment, providing these children even from developing countries or regions access to intensified multimodal treatments earlier and thus improving their long-term outcome.
尽管已经多次尝试为高危神经母细胞瘤（NB）患者（诊断时年龄≥18 个月且有转移性 NB）制定风险分层，但“超高危 NB”的定义仍缺乏共识，并且用于识别该亚组的指标仍不清楚。本研究旨在开发一种基于易于获得的血液衍生生物因子的列线图，用于识别在 3 或 5 年内死亡风险最高的超高危 NB 患者。
我们招募了 2015 年至 2023 年在中山大学肿瘤防治中心接受治疗的 167 例 NB 患者，并将其随机分为训练集和验证集（分别为 116 例和 51 例）。在训练集中进行单因素和多因素 Cox 分析，以筛选出用于构建预测 1 年、3 年和 5 年总生存率（OS）列线图模型的独立预后指标。通过一致性指数（C 指数）和校准图评估列线图在训练集和验证集的判别能力。基于列线图模型获得的风险评分，进一步使用受试者工作特征（ROC）曲线下面积（AUC）评估训练集和验证集中 1 年、3 年和 5 年 OS 率的预后准确性。
通过单因素和多因素 Cox 分析，在训练集中确定了独立的预后指标，包括血清乳酸脱氢酶（LDH）和白蛋白（ALB），并用于建立列线图模型。该模型具有良好的判别能力，训练队列中的 C 指数为 0.706（95%CI：0.633-0.788）。根据基于建立的列线图计算的切点，得分>34 分的患者可分为超高危 NB 亚组，该亚组的 OS 明显差于非超高危组（p<0.001）。训练集中 3 年和 5 年 OS 率的 ROC 曲线 AUC 值分别为 0.758 和 0.756。此外，基于训练集中建立的切点（34 分），该模型在验证集中的 C 指数为 0.773（95%CI：0.664-0.897），3 年和 5 年 OS 率的 AUC 值分别为 0.825 和 0.826，具有良好的判别能力和强大的预后准确性。
我们开发了一种基于常用实验室指标的简单易用的列线图，用于在治疗前识别超高危 NB 亚组，为这些儿童甚至来自发展中国家或地区的儿童提供更早接受强化多模式治疗的机会，从而改善他们的长期预后。
