Quantitative modeling of mortality patterns in dogs exposed to alpha particle emitting radionuclides: Insights from competing risks and causal inference machine learning - Eric Wang; Igor Shuryak; David J. Brenner (PLoS ONE, 2025)
This study employed state-of-the-art machine learning to evaluate the mortality effects of alpha-emitting radionuclides (241Am, 249Cf, 252Cf, 238Pu, 239Pu, 224Ra, 226Ra, 228Th) on 2,576 dogs, factoring in radioactivity levels, composition, administration method (injection or inhalation), and age at exposure. There were 972 cancer deaths, 599 non-cancer deaths, 789 deaths from many diseases (involving several diagnoses, including both cancer and non-cancer pathologies), and 216 deaths with uncertain causes. A Random Survival Forest model for overall mortality achieved concordance scores of 0.763 and 0.745 on training and testing data subsets, respectively. A model variant with competing risks was used to investigate mortality trends over time for different disease categories. It achieved concordances of 0.814 for cancer, 0.652 for non-cancer, and 0.778 for many diseases on training data, and 0.817 for cancer, 0.651 for non-cancer, and 0.780 for many diseases on testing data. All radionuclides exhibited radiation responses for cancer, with 226Ra and 239Pu showing the strongest effects. Some responses were non-linear, with indications of saturation or downturn at high treatment quantities. For non-cancer diseases, radiation responses were generally weaker and more variable. For the many diseases endpoint, 238Pu and 239Pu demonstrated the strongest response patterns, with 239Pu exhibiting greater lethality via inhalation compared to injection.. Using a Causal Forest model, which is designed to detect causal relationships rather than just associations, we investigated the causal impact of radioactivity on dog mortality, accounting for other variables. We found a significant (p < 2 × 10−16) negative average causal effect of −1,375 days per log10 radioactivity unit on survival time. This study improves current knowledge of cancer and non-cancer mortality patterns from densely-ionizing radiation in mammals by using machine learning to analyze combined historical data on dogs exposed to different radionuclides, modeling multiple variables, nonlinear dependencies, and causal relationships.