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

Abstract

Purpose A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RE-CIST or RANO criteria, is tedious, time-consuming, and can miss important tumor response information; most notably, such criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, 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. Following recent work in which we used DNE to train the parameters of a small convolutional neural network (CNN) for MRI sequence identification, we have now used a DNE parameter search to optimize a CNN 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 generation's CNN, repeating