A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
Journal of cancer research and therapeutics - 19(2023), 5 vom: 29. Juli, Seite 1219-1225 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Amiri, Sepideh [VerfasserIn] |
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| Links: |
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| Themen: |
Auditory brain stem response |
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| Anmerkungen: |
Date Completed 02.11.2023 Date Revised 02.11.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.4103/jcrt.jcrt_2298_21 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM362810931 |
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| 100 | 1 | |a Amiri, Sepideh |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Completed 02.11.2023 | ||
| 500 | |a Date Revised 02.11.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Auditory brain stem response | |
| 650 | 4 | |a computed tomography | |
| 650 | 4 | |a head-and-neck cancer | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a radiation therapy | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Abdolali, Fatemeh |e verfasserin |4 aut | |
| 700 | 1 | |a Neshastehriz, Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Nikoofar, Alireza |e verfasserin |4 aut | |
| 700 | 1 | |a Farahani, Saeid |e verfasserin |4 aut | |
| 700 | 1 | |a Firoozabadi, Leila Alipour |e verfasserin |4 aut | |
| 700 | 1 | |a Askarabad, Zahra Alaei |e verfasserin |4 aut | |
| 700 | 1 | |a Cheraghi, Susan |e verfasserin |4 aut | |
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