Yan Zhiwei, Wu Yumeng, Chen Yuehua, Xu Jian, Zhang Xiubing, Yin Qiyou
Department of Paediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China.
Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, Nantong, China.
Front Pediatr. 2024 Sep 19;12:1417818. doi: 10.3389/fped.2024.1417818. eCollection 2024.
Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions.
We built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases.
The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (< 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma.
The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.
神经母细胞瘤（NB）发生远处转移的患者通常预后较差，早期诊断对于预防远处转移至关重要。本研究旨在开发一种机器学习模型，用于预测神经母细胞瘤患者发生远处转移的风险，以辅助临床诊断和治疗决策。
我们使用2010年至2018年监测、流行病学和最终结果（SEER）数据库中1542例神经母细胞瘤患者的数据构建了一个预测模型。采用七种机器学习方法来预测神经母细胞瘤远处转移的可能性。单因素和多因素逻辑回归分析用于确定构建机器学习模型的独立危险因素。其次，使用受试者工作特征曲线下面积（AUC）、精确召回率（PR）曲线、决策曲线分析（DCA）和校准曲线来评估模型性能。为了进一步解释最佳模型，应用了Shapley值相加解释法（SHAP）。最终，使用最佳模型创建了一个在线计算器，用于估计神经母细胞瘤远处转移的可能性。
该研究纳入了1542例神经母细胞瘤患者，多因素逻辑回归分析显示，年龄、组织学类型、肿瘤大小、肿瘤分级、原发部位、手术、化疗和放疗是神经母细胞瘤远处转移的独立危险因素（<0.05）。在构建的七种算法中，逻辑回归（LR）被发现是最优算法，在训练集和验证集中的AUC值最高，分别为0.835和0.850。最后，我们使用逻辑回归模型构建了一个神经母细胞瘤远处转移的网络计算器。
本研究基于临床和病理信息开发并验证了一种机器学习模型，用于预测神经母细胞瘤患者发生远处转移的风险，这可能有助于医生做出临床决策。
