Direct evaluation of treatment response in brain metastatic disease with deep neuroevolution Journal Article
| Authors: | Stember, J. N.; Young, R. J.; Shalu, H. |
| Article Title: | Direct evaluation of treatment response in brain metastatic disease with deep neuroevolution |
| Abstract: | Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. 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 and time-consuming, and can miss important tumor response information. Most notably, the prevalent response 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) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. 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 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's 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 cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown that 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 ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis. |
| Journal Title: | Journal of Digital Imaging |
| Volume: | 36 |
| Issue: | 2 |
| ISSN: | 0897-1889 |
| Publisher: | Springer |
| Date Published: | 2023-04-01 |
| Start Page: | 536 |
| End Page: | 546 |
| Language: | English |
| DOI: | 10.1007/s10278-022-00725-5 |
| PROVIDER: | EBSCOhost |
| PROVIDER: | cinahl |
| PMCID: | PMC10039135 |
| PUBMED: | 36396839 |
| DOI/URL: | |
| Notes: | Accession Number: 162679403 -- Entry Date: In Process -- Revision Date: 20230330 -- Publication Type: Article -- Journal Subset: Allied Health; Biomedical; Computer/Information Science; Double Blind Peer Reviewed; Peer Reviewed; USA -- NLM UID: 9100529. -- Source: Cinahl |
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