BACKGROUND We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis. METHODS The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AUCs), as was the concordance index (c-index). RESULTS The subjects (n = 1088) were separated into training (80%, n = 870) and test sets (20%, n = 218). The training data were randomly sorted into training and validation sets using 5-fold cross validation. The median c-index of the five validation sets was 0.84 (95% confidence interval 0.79-0.87). The median AUC of the five validation subsets was 0.84. This model was evaluated with the previously separated test set. The c-index was 0.82 and the mean AUC of the 30 different time windows was 0.85 (standard deviation 0.02). According to the estimated survival probability (by 62 months), we divided the test group into five subgroups. The survival curves of the subgroups showed statistically significant separation (p < 0.001). CONCLUSIONS This study is the first to analyze population-level data using artificial neural network ML algorithms for the role and outcomes of surgical resection and radiation therapy in spino-pelvic chondrosarcoma.

中文翻译：

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使用深度生存神经网络的脊柱和盆腔软骨肉瘤患者的新型预后。

背景技术我们使用监测，流行病学和最终结果（SEER）数据库来开发和验证深度生存神经网络机器学习（ML）算法，以预测脊柱盆腔软骨肉瘤诊断后的生存率。方法使用SEER 18注册中心在模型中应用风险估计距离生存神经网络（RED_SNN）。我们在每个时间窗口使用接收器工作特性曲线和曲线下面积（AUC）评估了模型，一致性指标（c-index）也是如此。结果受试者（n = 1088）被分为训练（80％，n = 870）和测试集（20％，n = 218）。使用5倍交叉验证将训练数据随机分为训练和验证集。五个验证集的中位c指数为0.84（95％置信区间0.79-0.87）。五个验证子集的AUC中位数为0.84。使用先前分离的测试集评估了该模型。c指数为0.82，30个不同时间窗口的平均AUC为0.85（标准差0.02）。根据估计的生存概率（62个月），我们将测试组分为五个亚组。亚组的存活曲线显示统计学上显着的分离（p <0.001）。结论这项研究是第一个使用人工神经网络ML算法分析人群水平数据在脊柱骨盆软骨肉瘤手术切除和放射治疗中的作用和结果。根据估计的生存概率（62个月），我们将测试组分为五个亚组。各亚组的生存曲线显示出统计学上的显着差异（p <0.001）。结论这项研究是第一个使用人工神经网络ML算法分析人群水平数据在脊柱骨盆软骨肉瘤手术切除和放射治疗中的作用和结果。根据估计的生存概率（62个月），我们将测试组分为五个亚组。各亚组的生存曲线显示出统计学上的显着差异（p <0.001）。结论这项研究是第一个使用人工神经网络ML算法分析人群水平数据在脊柱骨盆软骨肉瘤手术切除和放射治疗中的作用和结果。