A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy
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A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy. / Amiri, Sepideh; Abdolali, Fatemeh; Neshastehriz, Ali; Nikoofar, Alireza; Farahani, Saeid; Firoozabadi, Leila Alipour; Askarabad, Zahra Alaei; Cheraghi, Susan.
In: Journal of Cancer Research and Therapeutics, Vol. 19, No. 5, 2023, p. 1219-1225.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy
AU - Amiri, Sepideh
AU - Abdolali, Fatemeh
AU - Neshastehriz, Ali
AU - Nikoofar, Alireza
AU - Farahani, Saeid
AU - Firoozabadi, Leila Alipour
AU - Askarabad, Zahra Alaei
AU - Cheraghi, Susan
N1 - Publisher Copyright: © 2023 Journal of Cancer Research and Therapeutics.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Auditory brain stem response
KW - computed tomography
KW - head-and-neck cancer
KW - machine learning
KW - radiation therapy
KW - radiomics
U2 - 10.4103/jcrt.jcrt_2298_21
DO - 10.4103/jcrt.jcrt_2298_21
M3 - Journal article
C2 - 37787286
AN - SCOPUS:85173670520
VL - 19
SP - 1219
EP - 1225
JO - Journal of Cancer Research and Therapeutics
JF - Journal of Cancer Research and Therapeutics
SN - 0973-1482
IS - 5
ER -
ID: 391035451