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.

翻译：2020年全球新发近100万例头颈癌病例，这是一种致命且常见的恶性肿瘤。由于病灶多发于不同部位且患者预后存在差异，该癌症的临床决策与治疗面临诸多挑战。因此，自动分割与预后评估方法有助于确保每位患者获得最有效的治疗。本文提出一个可对任意视场（FoV）PET与CT配准扫描执行上述功能的框架，从而以团队\texttt{VokCow}身份参与HECKTOR 2022挑战赛任务1和2。该方法包含三个阶段：定位、分割与生存预测。首先，将任意FoV的扫描图像裁剪至头颈区域，并训练U型卷积神经网络（CNN）分割感兴趣区域。随后，利用所获区域，将另一个CNN与支持向量机分类器结合实现肿瘤语义分割，在任务1中取得0.57的聚合Dice评分。最后，通过集成Weibull加速失效时间模型与深度学习方法进行生存预测。除了患者健康记录数据，我们还探索了通过图卷积处理以肿瘤为中心图像块的图结构能否改善预后预测。测试集上的一致性指数达到0.64，在该任务挑战排行榜中位列第6。