Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma

鼻咽癌 接收机工作特性 颞叶 医学 逻辑回归 核医学 Lasso(编程语言) 内科学 放射治疗 心脏病学 癫痫 计算机科学 精神科 万维网
作者
Fu Gui,Yuanyao Xie,Jie Pan,Yingwei Qiu,Hualiang He,Zhipeng Li,Jing Li,Yanqiu Feng,Xiaofei Lv
出处
期刊:Radiotherapy and Oncology [Elsevier]
卷期号:173: 277-284 被引量:6
标识
DOI:10.1016/j.radonc.2022.06.008
摘要

Background To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. Methods 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose–volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. Results Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a “decrease-increase/recovery” pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. Conclusion Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.
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