Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma. (August 2022)

• Record Type: Journal Article
• Authors: Fu, Gui; Xie, Yuanyao; Pan, Jie; Qiu, Yingwei; He, Haoqiang; Li, Zhipeng; Li, Jing; Feng, Yanqiu; Lv, Xiaofei
• Is Part Of: Radiotherapy and oncology. Volume 173(2022)
• Page Start: 277
• Page End: 284
• Publication Date: 2022-08
• Subjects: RT radiotherapy -- NPC nasopharyngeal carcinoma -- HCs healthy controls -- RBI radiation-induced brain injury -- MoCA Montreal Cognitive Assessment -- DVH dose–volume-histogram -- GM gray matter -- FC functional connectivity -- Cp clustering coefficient -- Lp characteristic path length -- Eglobal global efficiency -- Eloc local efficiency -- DC degree centrality -- NE nodal efficiency -- s.d. standard deviation -- AUC area under the curve -- DMN default mode network -- LASSO least absolute shrinkage and selection operator -- NTCP normal tissue complication probability -- TL temporal lobe -- SFGdor superior frontal gyrus dorsolateral -- IFGoperc inferior frontal gyrus opercular part -- ORBinf inferior frontal gyrus, orbital part -- ROL rolandic operculum -- PHG parahippocampal gyrus -- TPOsup temporal pole: superior temporal gyrus -- IFGtriang inferior frontal gyrus, triangular part -- DCG median cingulate and paracingulate gyri -- FFG fusiform gyrus -- HES heschl gyrus -- STG superior temporal gyrus -- HIP hippocampus -- PreCG precentral gyrus -- ITG inferior temporal gyrus -- ORBsupmed superior frontal gyrus, medial orbital -- CUN cuneus -- BBB blood–brain barrier; Radiation-induced brain injury -- Nasopharyngeal carcinoma -- Resting-state fMRI -- Graph theoretical analysis -- Dose–volume histogram -- Normal tissue complication probability; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.06.008
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Resting-state fMRI detected early radiation-induced brain functional abnormalities. Brain nodal changes were dynamic, mainly manifesting an 'impairment-recovery' pattern. D 20cc was an important prognostic determinant for nodal changes of STG.R. Abstract: 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 D 20cc and V 20 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.