With nearly one million new cases diagnosed worldwide in 2020, head \& neck
cancer is a deadly and common malignity. There are challenges to decision
making and treatment of such cancer, due to lesions in multiple locations and
outcome variability between patients. Therefore, automated segmentation and
prognosis estimation approaches can help ensure each patient gets the most
effective treatment. This paper presents a framework to perform these functions
on arbitrary field of view (FoV) PET and CT registered scans, thus approaching
tasks 1 and 2 of the HECKTOR 2022 challenge as team \texttt{VokCow}. The method
consists of three stages: localization, segmentation and survival prediction.
First, the scans with arbitrary FoV are cropped to the head and neck region and
a u-shaped convolutional neural network (CNN) is trained to segment the region
of interest. Then, using the obtained regions, another CNN is combined with a
support vector machine classifier to obtain the semantic segmentation of the
tumours, which results in an aggregated Dice score of 0.57 in task 1. Finally,
survival prediction is approached with an ensemble of Weibull accelerated
failure times model and deep learning methods. In addition to patient health
record data, we explore whether processing graphs of image patches centred at
the tumours via graph convolutions can improve the prognostic predictions. A
concordance index of 0.64 was achieved in the test set, ranking 6th in the
challenge leaderboard for this task.

通过 U 型卷积神经网络进行头颈部区域分割，结合支持向量机分类器实现肿瘤的语义分割，采用 Weibull 加速失效时间模型和深度学习等方法进行存活预后预测，在图像块处理方面探索性地进行图形卷积，从而实现对头颈癌进行自动分割和预后预测，测试数据获得了 0.64 的协调指数，在 HECKTOR 2022 挑战排行榜中排名第六。