  Purpose: A core component of advancing cancer treatment research is assessing
response to therapy. Doing so by hand, for example as per RECIST or RANO
criteria, is tedious, time-consuming, and can miss important tumor response
information; most notably, they exclude non-target lesions. We wish to assess
change in a holistic fashion that includes all lesions, obtaining simple,
informative, and automated assessments of tumor progression or regression. Due
to often low patient enrolments in clinical trials, we wish to make response
assessments with small training sets. Deep neuroevolution (DNE) can produce
radiology artificial intelligence (AI) that performs well on small training
sets. Here we use DNE for function approximation that predicts progression
versus regression of metastatic brain disease.
  Methods: We analyzed 50 pairs of MRI contrast-enhanced images as our training
set. Half of these pairs, separated in time, qualified as disease progression,
while the other 25 images constituted regression. We trained the parameters of
a relatively small CNN via mutations that consisted of random CNN weight
adjustments and mutation fitness. We then incorporated the best mutations into
the next generations CNN, repeating this process for approximately 50,000
generations. We applied the CNNs to our training set, as well as a separate
testing set with the same class balance of 25 progression and 25 regression
images.
  Results: DNE achieved monotonic convergence to 100% training set accuracy.
DNE also converged monotonically to 100% testing set accuracy.
  Conclusion: DNE can accurately classify brain-metastatic disease progression
versus regression. Future work will extend the input from 2D image slices to
full 3D volumes, and include the category of no change. We believe that an
approach such as our could ultimately provide a useful adjunct to RANO/RECIST
assessment.
