  Overall survival (OS) time prediction is one of the most common estimates of
the prognosis of gliomas and is used to design an appropriate treatment
planning. State-of-the-art (SOTA) methods for OS time prediction follow a
pre-hoc approach that require computing the segmentation map of the glioma
tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS
time. However, the training of the segmentation methods require ground truth
segmentation labels which are tedious and expensive to obtain. Given that most
of the large-scale data sets available from hospitals are unlikely to contain
such precise segmentation, those SOTA methods have limited applicability. In
this paper, we introduce a new post-hoc method for OS time prediction that does
not require segmentation map annotation for training. Our model uses medical
image and patient demographics (represented by age) as inputs to estimate the
OS time and to estimate a saliency map that localizes the tumor as a way to
explain the OS time prediction in a post-hoc manner. It is worth emphasizing
that although our model can localize tumors, it uses only the ground truth OS
time as training signal, i.e., no segmentation labels are needed. We evaluate
our post-hoc method on the Multimodal Brain Tumor Segmentation Challenge
(BraTS) 2019 data set and show that it achieves competitive results compared to
pre-hoc methods with the advantage of not requiring segmentation labels for
training.
