A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice

Sep 21, 2026

Uploaded by Noah

Explores how densely ionizing radiation affects competing non-cancer and cancer mortality outcomes, using historical lifespan data from ~21,000 fission-neutron–exposed mice. Applies random survival forest (RSF) to model nonlinear dependencies without relying on strict parametric assumptions, treating cancer and non-cancer as competing risks. Uses SHAP values and variable importance to interpret drivers, revealing dose–response trends with cancer predominance, saturation at high doses, and largely linear non-cancer responses.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Radiation context and motivation

Methods

Data source and radiation types

Random survival forest for competing risks

Model interpretation with SHAP

Results

Dose–response patterns for cancer vs non-cancer

Dose rate, sex, and age effects

Discussion

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice Eric Wang*, Igor Shuryak & David J. Brenner This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose–response trends, with cancer being the predominant cause of mortality. SHAP value dose–response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection. Densely-ionizing radiations, such as neutrons, are encountered in various fields, including space exploration, nuclear safety, and therapeutic applications1–7. While much of the existing research, including our own previous studies like Wang et al.8, has focused on the carcinogenic effects of these radiations9–13, there is a growing need to understand their impact on non-cancer diseases14–20. These diseases, particularly those affecting the cardiovascular and nervous systems, can contribute significantly to radiation-induced mortality and are crucial for designing effective radioprotection strategies20–25. As humans continue to explore space and undertake long-distance missions to other planets, understanding the effects of space radiations becomes increasingly important to ensure the health and safety of astronauts. In particular, understanding the effects of dose fractionation and protraction on non-cancer diseases induced by densely ionizing radiations, such as those found in space, is crucial for assessing whether prolonged radiation exposure over many months or years can be more (or less) detrimental than short-term exposure to a similar dose. Investigating the role of sex in radiation sensitivity to non-cancer and cancer diseases is also very important for estimating the risks to female vs. male personnel involved in activities where exposures to densely ionizing radiations occur, such as astronauts. More research in this area can fill this knowledge gap and improve our overall understanding of the health effects of radiation. This is particularly important for public health and radiation risk assessment. The Janus archive26 is a comprehensive dataset that provides unique insights into the impact of densely ionizing radiation on mortality from cancers and various organ dysfunctions. The data is based on lifespan studies of laboratory rodents, specifically B6CF1 mice, exposed to fission neutrons. Neutrons can be considered a form of densely-ionizing radiation, whereas photons (X or gamma rays) with energies typically used for animal experiments can be considered sparsely-ionizing. These radiation types deposit energy differently on a microscopic level due to their distinct interactions with matter. Neutrons are uncharged particles and do not interact w

FAQs

What model is used to analyze competing cancer and non-cancer mortality outcomes?

The study employs random survival forest (RSF), a machine learning approach suited for nonlinear relationships and interactions while treating cancer and non-cancer outcomes as competing risks.

How do cancer and non-cancer dose–response patterns differ?

Cancer shows clear dose–response trends with SHAP shapes saturating at high doses (> 2 Gy), whereas non-cancer responses remain more linear across the full dose range.

What do the authors find about sex and age effects?

Sex and age effects appear less pronounced compared with dose-dependent patterns, indicating smaller contributions to the variability in mortality outcomes.

Sep 21, 2026

Uploaded by Noah