Abstract：
To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification.
This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature.
Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk.
The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.
This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature.
Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk.
The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.
摘要：
目的：开发一种基于计算机断层扫描（CT）的深度学习模型，以预测小细胞肺癌（SCLC）患者的总生存期（OS），并基于OS特征风险分层识别可从预防性颅脑照射（PCI）中受益的患者。
方法：本研究包括来自三个医疗中心的556例SCLC患者。训练,内部验证,外部验证队列包括309、133和114名患者，分别。OS签名是使用统一的全连接神经网络构建的。开发了基于操作系统签名的深度学习模型。开发了临床模型和组合模型，并将其与深度学习模型进行了比较。此外,在使用OS签名进行分层后评估PCI的益处.
结果：在内部和外部验证队列中，深度学习模型（一致性指数[C指数]0.745，0.733）在预测OS方面远远优于临床模型（C指数：0.635，0.630），但略差于组合模型(C指数：0.771，0.770)。此外,深度学习模型具有出色的校准，临床有用性，并提高了对生存结果分类的准确性。值得注意的是,高危患者在有限阶段和广泛阶段均可从PCI获得生存获益(均P<0.05),而在低风险患者中未观察到显著关联.
结论：基于CT的深度学习模型在预测SCLC患者的OS方面表现出良好的性能。OS签名可以帮助个性化治疗计划以选择可能受益于PCI的患者。
方法：本研究包括来自三个医疗中心的556例SCLC患者。训练,内部验证,外部验证队列包括309、133和114名患者，分别。OS签名是使用统一的全连接神经网络构建的。开发了基于操作系统签名的深度学习模型。开发了临床模型和组合模型，并将其与深度学习模型进行了比较。此外,在使用OS签名进行分层后评估PCI的益处.
结果：在内部和外部验证队列中，深度学习模型（一致性指数[C指数]0.745，0.733）在预测OS方面远远优于临床模型（C指数：0.635，0.630），但略差于组合模型(C指数：0.771，0.770)。此外,深度学习模型具有出色的校准，临床有用性，并提高了对生存结果分类的准确性。值得注意的是,高危患者在有限阶段和广泛阶段均可从PCI获得生存获益(均P<0.05),而在低风险患者中未观察到显著关联.
结论：基于CT的深度学习模型在预测SCLC患者的OS方面表现出良好的性能。OS签名可以帮助个性化治疗计划以选择可能受益于PCI的患者。
