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使用深度学习开发和验证松芽细胞瘤患者的生存预测模型:基于SEER的研究

Xuanzi Li¹, Shuai Yang², Yingpeng Peng¹

¹The Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.

Cancer reports (Hoboken, N.J.)

|August 7, 2025

概括

深度学习模型准确地预测了松芽细胞瘤患者的3年生存期,超过了传统的Cox比例危险模型. 这一进步为这种罕见的中枢神经系统瘤提供了更好的预后见解.

科学领域:

神经瘤学神经瘤学
计算生物学是一种计算生物学.
医学数据科学医学数据科学

背景情况:

松芽细胞瘤 (PBs) 是罕见的儿科中枢神经系统瘤,预后数据有限.
缺少PB生存结果的现有预测模型.
准确的预后对于指导治疗和管理患者期望至关重要.

研究的目的:

开发和验证深度学习 (DL) 模型,用于预测松叶母细胞瘤患者的3年总生存率 (OS) 和疾病特异性生存率 (DSS).
将DL模型的预测性能与传统的Cox比例危险 (CPH) 模型进行比较.

主要方法:

从监测,流行病学和最终结果 (SEER) 数据库 (1975-2019) 中确定了被诊断患有松芽细胞瘤的患者.
深度神经网络 (DNN) 被训练并使用5倍交叉验证进行测试.
为了进行比较分析,构建了多变量CPH模型. 模型性能使用ROC曲线分析和校准图进行评估.

主要成果:

这项研究包括145名患有松芽细胞瘤的患者.
DNN模型在3年后的OS (AUC=0.92) 和DSS (AUC=0.76) 中实现了高预测准确度.
开发的DNN模型证明了OS和DSS预测的良好校准.

结论:

与CPH模型相比,深度学习模型显著提高了松芽细胞瘤患者生存结果的预测准确性.
这些发现突显了DL算法在改善罕见瘤类型的预后能力方面的潜力.
开发的DL模型可以帮助更好地预测细胞瘤的结果和临床决策.

关键词:

塞尔 (Seer) 是一个明星.人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.松叶细胞瘤 (pineoblastoma) 是一种

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