Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.

Autor:	Ahmed SBS; Department of Physics, Florida Atlantic University, Boca Raton, FL, United States., Naeem S; Gamma Knife Radiosurgery Center, Dow University of Health Sciences, Karachi, Pakistan., Khan AMH; Radiation Oncology, McGill University, Montreal, QC, Canada., Qureshi BM; Radiation Oncology, Aga Khan University Hospital, Karachi, Pakistan., Hussain A; Tawam Hospital, Al Ain, United Arab Emirates., Aydogan B; Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States., Muhammad W; Department of Physics, Florida Atlantic University, Boca Raton, FL, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2024 Apr 05; Vol. 7, pp. 1329737. Date of Electronic Publication: 2024 Apr 05 (Print Publication: 2024).
DOI:	10.3389/frai.2024.1329737
Abstrakt:	Background and Purpose: We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods: Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results: The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion: We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Ahmed, Naeem, Khan, Qureshi, Hussain, Aydogan and Muhammad.)
Databáze:	MEDLINE
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