ArticleScientific reports2023
Machine learning approach for quantitative biodosimetry of partial-body or total-body radiation exposures by combining radiation-responsive biomarkers.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Beam intensity and stability control on a modified clinical linear accelerator for FLASH irradiation.Physics in medicine and biology · 2025Article
- Partial-body Models of Radiation Exposure.Radiation research · 2025Review
- Intracellular lymphocyte protein biomarkers for early radiological triage in the human population.PloS one · 2025Article
- Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice.Scientific reports · 2024Article
- BAX and DDB2 as biomarkers for acute radiation exposure in the human blood ex vivo and non-human primate models.Scientific reports · 2024Article
- Comparison of Novel Proteomic Expression Profiles for Radiation Exposure in Male and Female C57BL6 Mice.Radiation research · 2024Article
- Biomarker integration for improved biodosimetry of mixed neutron + photon exposures.Scientific reports · 2023Article
- Performance Evaluation of Radiation-Shielding Materials and Process Technology for Manufacturing Skin Protection Cream.Materials (Basel, Switzerland) · 2023Article
- Assessment of Micronuclei Frequency in the Peripheral Blood of Adult and Pediatric Patients Receiving Fractionated Total Body Irradiation.Cytogenetic and genome research · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
During a large-scale radiological event such as an improvised nuclear device detonation, many survivors will be shielded from radiation by environmental objects, and experience only partial-body irradiation (PBI), which has different consequences, compared with total-body irradiation (TBI). In this study, we tested the hypothesis that applying machine learning to a combination of radiation-responsive biomarkers (ACTN1, DDB2, FDXR) and B and T cell counts will quantify and distinguish between PBI and TBI exposures. Adult C57BL/6 mice of both sexes were exposed to 0, 2.0-2.5 or 5.0 Gy of half-body PBI or TBI. The random forest (RF) algorithm trained on ½ of the data reconstructed the radiation dose on the remaining testing portion of the data with mean absolute error of 0.749 Gy and reconstructed the product of dose and exposure status (defined as 1.0 × Dose for TBI and 0.5 × Dose for PBI) with MAE of 0.472 Gy. Among irradiated samples, PBI could be distinguished from TBI: ROC curve AUC = 0.944 (95% CI: 0.844-1.0). Mouse sex did not significantly affect dose reconstruction. These results support the hypothesis that combinations of protein biomarkers and blood cell counts can complement existing methods for biodosimetry of PBI and TBI exposures.
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