Predicting the risk of neurocognitive decline after brain irradiation in adult patients with a primary brain tumor

Background Introduction:

Radiotherapy is a major treatment modality for brain tumor patients, but it can cause cognitive decline as a side effect, which is one of the most concerning complications. Currently, there is a lack of tools in clinical practice to assess the risk of cognitive decline in patients. The purpose of this study is to establish a risk prediction model using clinical and dose-volume factors, providing a basis for optimizing radiotherapy regimens and postoperative rehabilitation.

Research Institutions and Authors:

This study was conducted by the Department of Radiation Oncology (MAASTRO Clinic) and the School of Oncology and Developmental Biology (GROW) at Maastricht University Medical Center in the Netherlands. The first author is Fariba Tohidinezhad, and the corresponding author is Dr. Alberto Traverso. The authors also come from the Department of Neuropsychology, Neurology, and other related departments at Maastricht University Medical Center.

Research Methods:

1) Subjects: 219 patients with primary brain tumors who received curative radiotherapy (photon or proton) between 2019 and 2022.

2) Assessment tools: The Controlled Oral Word Association Test (COWA), Hopkins Verbal Learning Test-Revised (HVLTR), and Trail Making Test (TMT) were used to objectively assess cognitive decline.

3) Predictive factors: Clinical factors such as demographics, tumor characteristics, treatment information, comorbidities, and medication use, as well as dose-volume parameters for multiple brain regions, were considered.

4) Statistical analysis: After univariate analysis, multivariate logistic regression was used to establish clinical models, dose-volume models, and comprehensive models. Internal validation was performed for discriminative ability (area under the ROC curve, AUC), calibration ability (mean absolute error, MAE), and decision curve analysis (DCA) of net benefit.

Key Results:

1) At 6 months, 1 year, and 2 years, 50%, 44.5%, and 42.7% of patients experienced cognitive decline, respectively.

2) The predictive factors for cognitive decline at 6 months in the comprehensive model included age >56 years, overweight, obesity, chemotherapy, brain V20Gy ≥20%, brainstem volume ≥26cc, and pituitary volume ≥0.5cc.

3) In the 1-year cognitive decline model, temporal lobe tumors and left hippocampal maximum dose ≥7Gy were risk factors.

4) Risk factors for cognitive decline at 2 years included brain maximum dose ≥54Gy and cerebellar maximum dose ≥27Gy.

5) Decision curve analysis showed that the comprehensive model had the highest net benefit, with AUCs of 0.79, 0.72, and 0.69 at 6 months, 1 year, and 2 years, respectively.

Research Significance:

This study established a risk prediction model for cognitive decline, incorporating clinical and dose-volume factors. It can be used for individualized risk assessment and provide a basis for optimizing radiotherapy strategies, screening high-risk patients, and implementing rehabilitation interventions. The model suggests that hippocampal and cerebellar dose protection may reduce the risk of cognitive decline. These findings provide new evidence for optimizing radiotherapy regimens.

Research Highlights:

1) This is the first study to establish a risk prediction model for cognitive decline in adults, incorporating comprehensive predictive factors.

2) Standardized neuropsychological tests were used to objectively assess cognitive function.

3) Dose-volume parameters for multiple brain regions were analyzed, elucidating their relationship with cognitive impairment.

4) The prediction model demonstrated good discriminative ability, calibration ability, and decision net benefit.

5) The prediction model is easy to apply in clinical practice and can provide a reference for clinical decision-making and rehabilitation interventions.

This research aids in assessing the risk of cognitive impairment caused by radiotherapy and provides decision support for individualized treatment and interventions.