  We report a model to predict patient's radiological response to curative
radiation therapy (RT) for non-small-cell lung cancer (NSCLC).
  Cone-Beam Computed Tomography images acquired weekly during the six-week
course of RT were contoured with the Gross Tumor Volume (GTV) by senior
radiation oncologists for 53 patients (7 images per patient).
  Deformable registration of the images yielded six deformation fields for each
pair of consecutive images per patient.
  Jacobian of a field provides a measure of local expansion/contraction and is
used in our model.
  Delineations were compared post-registration to compute unchanged ($U$),
newly grown ($G$), and reduced ($R$) regions within GTV.
  The mean Jacobian of these regions $\mu_U$, $\mu_G$ and $\mu_R$ are
statistically compared and a response assessment model is proposed.
  A good response is hypothesized if $\mu_R < 1.0$, $\mu_R < \mu_U$, and $\mu_G
< \mu_U$.
  For early prediction of post-treatment response, first, three weeks' images
are used.
  Our model predicted clinical response with a precision of $74\%$.
  Using reduction in CT numbers (CTN) and percentage GTV reduction as features
in logistic regression, yielded an area-under-curve of 0.65 with p=0.005.
  Combining logistic regression model with the proposed hypothesis yielded an
odds ratio of 20.0 (p=0.0).
