AUTHOR=Zhang You-ming , Kang Ya-fei , Zeng Jun-jie , Li Li , Gao Jian-ming , Liu Li-zhi , Shi Liang-rong , Liao Wei-hua 

TITLE=Surface-Based Falff: A Potential Novel Biomarker for Prediction of Radiation Encephalopathy in Patients With Nasopharyngeal Carcinoma

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.692575

DOI=10.3389/fnins.2021.692575

ISSN=1662-453X

ABSTRACT=Radiation encephalopathy (RE) is an important potential complication in patients with nasopharyngeal carcinoma (NPC) who undergo radiotherapy (RT) that can affect quality of life. However, a functional imaging biomarker of pre-symptomatic RE has not yet been established. This study aimed to assess radiation-induced gray matter functional alterations and explore fractional amplitude of low-frequency fluctuation (fALFF) as an imaging biomarker for predicting or diagnosing RE in NPC patients. A total of 60 NPC patients were examined, 21 in the pre-RT cohort and 39 in the post-RT cohort. The post-RT patients were further divided into two subgroups according to occurrence of RE in follow-up: post-RT non-RE (n = 21) and post-RT RE proved in follow-up (n = 18). Surface-based and volume-based fALFF were used to detect radiation-induced functional alterations. Functional derived features were then adopted to construct a predictive model for diagnosis of RE. We observed that surface-based fALFF could sensitively detect radiation-induced functional alterations in intratemporal brain regions (such as the hippocampus and superior temporal gyrus), as well as extratemporal regions (such as the insula and prefrontal lobe); however, no significant intergroup differences were observed using volume-based fALFF. No significant correlation between fALFF and radiation dose to the ipsilateral temporal lobe was observed. Support vector machine analysis revealed that surface-based fALFF in the bilateral superior temporal gyri and left insula exhibited impressive performance (accuracy = 80.49%) in identifying patients likely to develop RE. We conclude that surface-based fALFF may serve as a sensitive imaging biomarker in prediction of RE.