本研究的目的是探讨将癌周区域纳入深度神经网络训练是否可以提高模型在预测鼻咽癌预后方面的表现。共回顾性纳入了381例分为高风险组和低风险组的鼻咽癌患者。使用Deeplab v3和U-Net进行训练，建立了肿瘤和可疑淋巴结的自动分割模型。通过从自动分割区域边缘向外扩展5、10、20、40和60个像素，构建了5个数据集。使用Inception-Resnet-V2、ECA-ResNet50t、EfficientNet-B3和EfficientNet-B0三种模型，分别用原始图像、分割图像和五个新构建的数据集进行训练，建立分类模型。使用接收器操作特征曲线来评估每个模型的表现。 Deeplab v3和U-Net的Dice系数分别为0.741（95％CI：0.722-0.760）和0.737（95％CI：0.720-0.754）。使用原始图像、分割图像以及扩展5、10、20、40和60个像素的图像训练的深度学习模型的平均曲线下面积（aAUCs）分别为0.717±0.043，0.739±0.016，0.760±0.010，0.768±0.018，0.802±0.013，0.782±0.039和0.753±0.014。扩展20像素训练的模型表现最佳。癌周区域包含与预后相关的信息，纳入此区域能够提高深度学习模型预后预测的表现。©2023作者。

The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC.A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model.The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance.The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.© 2023. The Author(s).