Figure 1.

Schematic diagram of the survival prediction framework. The proposed framework consists of two major components: the U-Net segmentation network and the survival prediction model. The U-Net is trained with PET/CT images and corresponding physician contours but without survival-related information. The “dimensional bottleneck” at the middle of the U-Net produces latent variables summarizing image features (55,296 features from CT + 55,296 features from PET), which we hypothesize to be potentially relevant to cancer survival. These features are then clustered by k-medoids clustering approach in an unsupervised manner. Next, the LASSO method is used to select medoid features from the clusters based on their associations with survival. Last, a logistic regression model is trained for survival prediction, and survival prediction is performed when a new patient arrives with features extracted from the same U-Net.