AUTHOR=Hou Jing , He Yun , Li Handong , Lu Qiang , Lin Huashan , Zeng Biao , Xie Chuanmiao , Yu Xiaoping 

TITLE=MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy

JOURNAL=Frontiers in Neurology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1344324

DOI=10.3389/fneur.2024.1344324

ISSN=1664-2295

ABSTRACT=Objective: To construct radiomics models based on MRI at different time points for the early prediction of cystic brain radionecrosis (CBRN) for nasopharyngeal carcinoma (NPC). 
Methods: A total of 202 injured temporal lobes from 155 NPC patients with radiotherapy-induced temporal lobe injury (RTLI) after intensity modulated radiotherapy (IMRT) were included in the study. All the injured lobes were randomly divided into the training (n = 143) and validation (n = 59) sets. Radiomics models were constructed by using features extracted from T2WI at two different time points: at the end of IMRT (post-IMRT) and the first-detected RTLI (first-RTLI). A delta-radiomics feature was defined as the percentage change in a radiomics feature from post-IMRT to first-RTLI. The radiomics nomogram was constructed by combining clinical risk factors and radiomics signatures using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic analysis, and the area under the curve (AUC) values of the different models were compared by DeLong test. 
Results: The post-IMRT, first-RTLI, and delta-radiomics models yielded AUC values of 0.84 (95% CI: 0.76-0.92), 0.86 (95% CI: 0.78-0.94), and 0.77 (95% CI: 0.67-0.87), respectively. The nomogram, which combined the history of drinking and radiomics signatures exhibited the highest AUC of 0.91 (95% CI: 0.85-0.97) compared to any single radiomics model. However, there were no statistically significant differences between the nomogram and post-IMRT radiomics model, nor between the nomogram and first-RTLI radiomics model.
Conclusion: Both radiomic models based on MRI at the end of IMRT and the first-detected RTLI, as well as radiomics nomogram model showed similarly excellent prediction potential. CBRN can be predicted at an earlier time after the completion of IMRT rather than at the first-detected RTLI.