Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates.

Autor: Voon NS; Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia.; National Cancer Institute, Ministry of Health, Jalan P7, Presint 7, 62250, Putrajaya, Malaysia., Manan HA; Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, 56000, Kuala Lumpur, Malaysia., Yahya N; Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia. azrulyahya@ukm.edu.my.
Jazyk: angličtina
Zdroj: Journal of cancer survivorship : research and practice [J Cancer Surviv] 2024 Aug; Vol. 18 (4), pp. 1297-1308. Date of Electronic Publication: 2023 Apr 03.
DOI: 10.1007/s11764-023-01371-8
Abstrakt: Purpose: 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.
Methods: 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.
Results: 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).
Conclusion: 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.
Implications for Cancer Survivors: 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.)
Databáze: MEDLINE