Abstract
For acute lymphoblastic leukemia treatment monitoring, the ratio of cancerous blood cells, called Minimal Residual Disease (MRD), is in practice assessed manually by experts. Using flow cytometry, single cells are classified as cancerous or healthy, based on a number of measured parameters. This task allows application of machine learning techniques, such as Stacked Denoising Autoencoders (DSAE). Seven different models’ performance in assessing MRD was evaluated. Higher model complexity does not guarantee better performance. For all models, a high number of false positives biases the predicted MRD value. Therefore, cost-sensitive learning is proposed as a way of improving classification performance.
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Notes
- 1.
For the conducted randomized clinical trial AIEOP-BFM 2009, approximately 2000 ALL patients between age 1–18 years in 20 countries in and outside Europe were observed per year (see http://www.bfm-international.org/trials.php [assessed 2018-04-14]).
- 2.
See [19, 8.5.1] for a definition of the performance metrics.
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Acknowledgements
This work was supported by the European Commission FP7-PEOPLE-2013-IAPP 610872 and by ZIT Life Sciences 2014 (1207843). We want to thank Michael Dworzak and Angela Schumich at the Children Cancer Research Center in Vienna for annotating and providing the FCM data.
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Scheithe, J., Licandro, R., Rota, P., Reiter, M., Diem, M., Kampel, M. (2019). Monitoring Acute Lymphoblastic Leukemia Therapy with Stacked Denoising Autoencoders. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_19
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