  Prediction of survivability in a patient for tumor progression is useful to
estimate the effectiveness of a treatment protocol. In our work, we present a
model to take into account the heterogeneous nature of a tumor to predict
survival. The tumor heterogeneity is measured in terms of its mass by combining
information regarding the radiodensity obtained in images with the gross tumor
volume (GTV). We propose a novel feature called Tumor Mass within a GTV (TMG),
that improves the prediction of survivability, compared to existing models
which use GTV. Weekly variation in TMG of a patient is computed from the image
data and also estimated from a cell survivability model. The parameters
obtained from the cell survivability model are indicatives of changes in TMG
over the treatment period. We use these parameters along with other patient
metadata to perform survival analysis and regression. Cox's Proportional Hazard
survival regression was performed using these data. Significant improvement in
the average concordance index from 0.47 to 0.64 was observed when TMG is used
in the model instead of GTV. The experiments show that there is a difference in
the treatment response in responsive and non-responsive patients and that the
proposed method can be used to predict patient survivability.
