Machine learning methodology for high throughput personalized neutron dose reconstruction in mixed neutron + photon exposures
Autor: | Guy Garty, Jay R. Perrier, Helen Turner, David J. Brenner, Monica Pujol-Canadell, Igor Shuryak |
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Rok vydání: | 2021 |
Předmět: |
Adult
Male Cell biology Photon Mean squared error Science Overfitting Radiation Radiation Dosage Machine learning computer.software_genre Article 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Biodosimetry Humans Neutron Lymphocytes Radiometry Neutrons Physics Photons Micronucleus Tests Multidisciplinary business.industry Computational Biology Radiation Exposure Healthy Volunteers High-Throughput Screening Assays Computational biology and bioinformatics Random forest 030220 oncology & carcinogenesis Medicine Probability distribution Female Artificial intelligence business computer Algorithms |
Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | We implemented machine learning in the radiation biodosimetry field to quantitatively reconstruct neutron doses in mixed neutron + photon exposures, which are expected in improvised nuclear device detonations. Such individualized reconstructions are crucial for triage and treatment because neutrons are more biologically damaging than photons. We used a high-throughput micronucleus assay with automated scanning/imaging on lymphocytes from human blood ex-vivo irradiated with 44 different combinations of 0–4 Gy neutrons and 0–15 Gy photons (542 blood samples), which include reanalysis of past experiments. We developed several metrics that describe micronuclei/cell probability distributions in binucleated cells, and used them as predictors in random forest (RF) and XGboost machine learning analyses to reconstruct the neutron dose in each sample. The probability of “overfitting” was minimized by training both algorithms with repeated cross-validation on a randomly-selected subset of the data, and measuring performance on the rest. RF achieved the best performance. Mean R2 for actual vs. reconstructed neutron doses over 300 random training/testing splits was 0.869 (range 0.761 to 0.919) and root mean squared error was 0.239 (0.195 to 0.351) Gy. These results demonstrate the promising potential of machine learning to reconstruct the neutron dose component in clinically-relevant complex radiation exposure scenarios. |
Databáze: | OpenAIRE |
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