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- Title
A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy.
- Authors
Amiri, Sepideh; Abdolali, Fatemeh; Neshastehriz, Ali; Nikoofar, Alireza; Farahani, Saeid; Firoozabadi, Leila Alipour; Askarabad, Zahra Alaei; Cheraghi, Susan
- Abstract
Objective: The present study aimed to assess machine learning (ML) models according to radiomic features to predict ototoxicity using auditory brain stem responses (ABRs) in patients with radiation therapy (RT) for head-and-neck cancers. Materials and Methods: The ABR test was performed on 50 patients having head-and-neck RT. Radiomic features were extracted from the brain stem in computed tomography images to generate a radiomic signature. Moreover, accuracy, sensitivity, specificity, the area under the curve, and mean cross-validation were used to evaluate six different ML models. Results: Out of 50 patients, 21 participants experienced ototoxicity. Furthermore, 140 radiomic features were extracted from the segmented area. Among the six ML models, the Random Forest method with 77% accuracy provided the best result. Conclusion: According to the ML approach, we showed the relatively high prediction power of the radiomic features in radiation-induced ototoxicity. To better predict the outcomes, future studies on a larger number of participants are recommended.
- Subjects
BRAIN stem; MACHINE learning; RADIOTHERAPY; FEATURE extraction; COMPUTED tomography
- Publication
Journal of Cancer Research & Therapeutics, 2023, Vol 19, Issue 5, p1219
- ISSN
0973-1482
- Publication type
Article
- DOI
10.4103/jcrt.jcrt_2298_21