Bibliographic Details
| Title: |
Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study. |
| Authors: |
Kauark-Fontes, Elisa1 (AUTHOR) elisakauark@gmail.com, Araújo, Anna Luiza Damaceno2 (AUTHOR), Andrade, Danilo Oliveira3 (AUTHOR), Faria, Karina Morais4 (AUTHOR), Prado-Ribeiro, Ana Carolina4 (AUTHOR), Laheij, Alexa5,6 (AUTHOR), Rios, Ricardo Araújo3 (AUTHOR), Ramalho, Luciana Maria Pedreira1 (AUTHOR), Brandão, Thais Bianca4 (AUTHOR), Santos-Silva, Alan Roger7 (AUTHOR) |
| Source: |
Supportive Care in Cancer. Feb2025, Vol. 33 Issue 2, p1-9. 9p. |
| Abstract: |
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy. Methods: Clinical data were collected from 157 patients with oral and oropharyngeal squamous cell carcinoma submitted to radiotherapy. Grade 2 OM or higher was considered (NCI). Two dataset versions were used; in the first version, all data were considered, and in the second version, a feature selection was added. Age, smoking status, surgery, radiotherapy prescription dose, treatment modality, histopathological differentiation, tumor stage, presence of oral cancer lesion, and tumor location were selected as key features. The training process used a fivefold cross-validation strategy with 10 repetitions. A total of 4 algorithms and 3 scaling methods were trained (12 models), without using data augmentation. Results: A comparative assessment was performed. Accuracy greater than 55% was considered. No relevant results were achieved with the first version, closest performance was Decision Trees with 52% of accuracy, 42% of sensitivity, and 60% of specificity. For the second version, relevant results were achieved, K-Nearest Neighbors outperformed with 64% accuracy, 58% sensitivity, and 68% specificity. Conclusion: ML demonstrated promising results in OM risk prediction. Model improvement was observed after feature selection. Best result was achieved with the KNN model. This is the first study to test ML for OM risk prediction using clinical data. [ABSTRACT FROM AUTHOR] |
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| Database: |
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