Abstract：
OBJECTIVE: To optimize our previously proposed TransRP, a model integrating CNN (convolutional neural network) and ViT (Vision Transformer) designed for recurrence-free survival prediction in oropharyngeal cancer and to extend its application to the prediction of multiple clinical outcomes, including locoregional control (LRC), Distant metastasis-free survival (DMFS) and overall survival (OS).
METHODS: Data was collected from 400 patients (300 for training and 100 for testing) diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) who underwent (chemo)radiotherapy at University Medical Center Groningen. Each patient\'s data comprised pre-treatment PET/CT scans, clinical parameters, and clinical outcome endpoints, namely LRC, DMFS and OS. The prediction performance of TransRP was compared with CNNs when inputting image data only. Additionally, three distinct methods (m1-3) of incorporating clinical predictors into TransRP training and one method (m4) that uses TransRP prediction as one parameter in a clinical Cox model were compared.
RESULTS: TransRP achieved higher test C-index values of 0.61, 0.84 and 0.70 than CNNs for LRC, DMFS and OS, respectively. Furthermore, when incorporating TransRP\'s prediction into a clinical Cox model (m4), a higher C-index of 0.77 for OS was obtained. Compared with a clinical routine risk stratification model of OS, our model, using clinical variables, radiomics and TransRP prediction as predictors, achieved larger separations of survival curves between low, intermediate and high risk groups.
CONCLUSIONS: TransRP outperformed CNN models for all endpoints. Combining clinical data and TransRP prediction in a Cox model achieved better OS prediction.
METHODS: Data was collected from 400 patients (300 for training and 100 for testing) diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) who underwent (chemo)radiotherapy at University Medical Center Groningen. Each patient\'s data comprised pre-treatment PET/CT scans, clinical parameters, and clinical outcome endpoints, namely LRC, DMFS and OS. The prediction performance of TransRP was compared with CNNs when inputting image data only. Additionally, three distinct methods (m1-3) of incorporating clinical predictors into TransRP training and one method (m4) that uses TransRP prediction as one parameter in a clinical Cox model were compared.
RESULTS: TransRP achieved higher test C-index values of 0.61, 0.84 and 0.70 than CNNs for LRC, DMFS and OS, respectively. Furthermore, when incorporating TransRP\'s prediction into a clinical Cox model (m4), a higher C-index of 0.77 for OS was obtained. Compared with a clinical routine risk stratification model of OS, our model, using clinical variables, radiomics and TransRP prediction as predictors, achieved larger separations of survival curves between low, intermediate and high risk groups.
CONCLUSIONS: TransRP outperformed CNN models for all endpoints. Combining clinical data and TransRP prediction in a Cox model achieved better OS prediction.
摘要：
目标：为了优化我们先前提出的TransRP，一个集成了CNN（卷积神经网络）和ViT（视觉变换器）的模型，设计用于口咽癌的无复发生存预测，并将其应用扩展到多种临床结果的预测，包括局部控制(LRC)，无远处转移生存期（DMFS）和总生存期（OS）。
方法：收集了在格罗宁根大学医学中心接受（化学）放疗的400名诊断为口咽鳞状细胞癌（OPSCC）患者（300名用于训练，100名用于测试）的数据。每个病人的数据包括治疗前的PET/CT扫描，临床参数,和临床结果终点，即LRC，DMFS和操作系统。在仅输入图像数据时，将TransRP的预测性能与CNN进行了比较。此外,比较了将临床预测因子纳入TransRP训练的三种不同方法(m1-3)和将TransRP预测作为临床Cox模型参数的一种方法(m4).
结果：TransRP比LRC的CNNs获得了更高的测试C指数值，分别为0.61、0.84和0.70，DMFS和操作系统，分别。此外，当将TransRP的预测纳入临床Cox模型(M4)时，获得了较高的OSC指数0.77。与OS的临床常规风险分层模型相比，我们的模型,使用临床变量，影像组学和TransRP预测作为预测因子，在低，中危和高危人群。
结论：TransRP优于所有端点的CNN模型。在Cox模型中结合临床数据和TransRP预测实现了更好的OS预测。
方法：收集了在格罗宁根大学医学中心接受（化学）放疗的400名诊断为口咽鳞状细胞癌（OPSCC）患者（300名用于训练，100名用于测试）的数据。每个病人的数据包括治疗前的PET/CT扫描，临床参数,和临床结果终点，即LRC，DMFS和操作系统。在仅输入图像数据时，将TransRP的预测性能与CNN进行了比较。此外,比较了将临床预测因子纳入TransRP训练的三种不同方法(m1-3)和将TransRP预测作为临床Cox模型参数的一种方法(m4).
结果：TransRP比LRC的CNNs获得了更高的测试C指数值，分别为0.61、0.84和0.70，DMFS和操作系统，分别。此外，当将TransRP的预测纳入临床Cox模型(M4)时，获得了较高的OSC指数0.77。与OS的临床常规风险分层模型相比，我们的模型,使用临床变量，影像组学和TransRP预测作为预测因子，在低，中危和高危人群。
结论：TransRP优于所有端点的CNN模型。在Cox模型中结合临床数据和TransRP预测实现了更好的OS预测。
