由鼻咽癌（NPC）放射治疗（RT）照射的脑区通常是不可避免的，这可能导致放射性认知缺陷。本研究旨在利用深度学习（DL）开发预测模型，通过远程评估预测患者在接受NPC RT后受损的认知能力，并确定其与生活质量（QoL）和MRI变化的关系。招募了70名年龄在20-76岁之间的患者进行MRI成像（治疗前后（6个月-1年），完整的认知评估。海马体、颞叶（TLs）和小脑被描绘，并提取了剂量学参数。通过电话（电话访问认知状况（TICS）、电话蒙特利尔认知评估（T-MoCA）、电话迷你亚丁布鲁克认知测验（Tele-MACE）和QLQ-H&N43）进行评估。回归和深度神经网络（DNN）模型用于预测利用解剖和治疗剂量特征进行的治疗后认知。远程认知评估相互关联（r > 0.9）。TLs 在治疗前后体积差异和认知缺陷上表现出显着性，与RT相关的体积萎缩和剂量分布相关。基于DNN接收操作曲线（AUROC）的认知预测良好的分类准确率（T-MoCA AUROC = 0.878，TICS AUROC = 0.89，Tele-MACE AUROC = 0.919）。使用远程评估进行DL预测模型可以帮助预测接受NPC RT后的认知缺陷。远程评估评估认知能力的可比结果表明其有可能替代标准评估。将预测模型应用于个体患者可以提供量身定制的干预措施，以管理NPC RT后的认知变化。©2023年，作者（s）在Springer Science+Business Media，LLC（Springer Nature的一部分）的独家许可下发表。

Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models in predicting compromised cognition in patients following NPC RT using remote assessments and determine their relation to the quality of life (QoL) and MRI changes.Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. Assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features.Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919).DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments.Application of prediction models in individual patient enables tailored interventions to be provided in managing cognitive changes following NPC RT.© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.