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Purpose <p>Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities.</p> Methods <p>This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE).</p> Results <p>Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and <i>ApOE4</i> genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5–200, <i>p</i> &lt; 0.001 and 2.85 [1.92–27.6], <i>p</i> = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. <i>ApoE4</i> allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: (<a href="https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/">https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/</a>)</p> Conclusion <p>Two pre-treatment laboratory values (elevated homocysteine and <i>ApOE</i> genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND.</p>

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Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Kyle Tuohy,
  • Aarav Badani,
  • Ava Franklin,
  • Brad Zacharia,
  • Dawit Aregawi,
  • Alireza Mansouri,
  • Paul Brown,
  • Michael Glantz

摘要

Purpose

Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities.

Methods

This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE).

Results

Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4 genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5–200, p < 0.001 and 2.85 [1.92–27.6], p = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4 allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: (https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/)

Conclusion

Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND.