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

Saad Bin Saeed Ahmed; Shahzaib Naeem; Agha Muhammad Hammad Khan; Bilal Mazhar Qureshi; Amjad Hussain; Bulent Aydogan; Wazir Muhammad

doi:10.3389/frai.2024.1329737

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

Front Artif Intell. 2024 Apr 5:7:1329737. doi: 10.3389/frai.2024.1329737. eCollection 2024.

Authors

Saad Bin Saeed Ahmed¹, Shahzaib Naeem², Agha Muhammad Hammad Khan³, Bilal Mazhar Qureshi⁴, Amjad Hussain⁵, Bulent Aydogan⁶, Wazir Muhammad¹

Affiliations

¹ Department of Physics, Florida Atlantic University, Boca Raton, FL, United States.
² Gamma Knife Radiosurgery Center, Dow University of Health Sciences, Karachi, Pakistan.
³ Radiation Oncology, McGill University, Montreal, QC, Canada.
⁴ Radiation Oncology, Aga Khan University Hospital, Karachi, Pakistan.
⁵ Tawam Hospital, Al Ain, United Arab Emirates.
⁶ Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States.

Abstract

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.

Keywords: artificial neural network; head and neck cancer; normal tissue complication probability; radiation therapy; tumor control probability.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Research reported in this publication was supported by the Proton International under Award Number C-23-262. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Proton International.