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

TL;DR

This paper demonstrates that Deep Neuroevolution can accurately classify progression or regression of brain metastatic disease from MRI images, offering a potential automated alternative to traditional assessment methods.

Contribution

The study introduces the use of Deep Neuroevolution for small-sample classification of tumor progression versus regression in brain metastases, achieving high accuracy.

Findings

Achieved 100% accuracy on training set

Achieved 100% accuracy on testing set

Demonstrated potential for automated tumor response assessment

Abstract

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.