  In this work, we aim to predict the survival time (ST) of glioblastoma (GBM)
patients undergoing different treatments based on preoperative magnetic
resonance (MR) scans. The personalized and precise treatment planning can be
achieved by comparing the ST of different treatments. It is well established
that both the current status of the patient (as represented by the MR scans)
and the choice of treatment are the cause of ST. While previous related
MR-based glioblastoma ST studies have focused only on the direct mapping of MR
scans to ST, they have not included the underlying causal relationship between
treatments and ST. To address this limitation, we propose a
treatment-conditioned regression model for glioblastoma ST that incorporates
treatment information in addition to MR scans. Our approach allows us to
effectively utilize the data from all of the treatments in a unified manner,
rather than having to train separate models for each of the treatments.
Furthermore, treatment can be effectively injected into each convolutional
layer through the adaptive instance normalization we employ. We evaluate our
framework on the BraTS20 ST prediction task. Three treatment options are
considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no
resection. The evaluation results demonstrate the effectiveness of injecting
the treatment for estimating GBM survival.
