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Purpose: A core component of advancing cancer treatment research is assessingresponse to therapy. Doing so by hand, for example as per RECIST or RANOcriteria, is tedious, time-consuming, and can miss important tumor responseinformation; most notably, they exclude non-target lesions. We wish to assesschange in a holistic fashion that includes all lesions, obtaining simple,informative, and automated assessments of tumor progression or regression. Dueto often low patient enrolments in clinical trials, we wish to make responseassessments with small training sets. Deep neuroevolution (DNE) can produceradiology artificial intelligence (AI) that performs well on small trainingsets. Here we use DNE for function approximation that predicts progressionversus regression of metastatic brain disease.  Methods: We analyzed 50 pairs of MRI contrast-enhanced images as our trainingset. Half of these pairs, separated in time, qualified as disease progression,while the other 25 images constituted regression. We trained the parameters ofa relatively small CNN via mutations that consisted of random CNN weightadjustments and mutation fitness. We then incorporated the best mutations intothe next generations CNN, repeating this process for approximately 50,000generations. We applied the CNNs to our training set, as well as a separatetesting set with the same class balance of 25 progression and 25 regressionimages.  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 progressionversus regression. Future work will extend the input from 2D image slices tofull 3D volumes, and include the category of no change. We believe that anapproach such as our could ultimately provide a useful adjunct to RANO/RECISTassessment.

Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution