TY - JOUR
T1 - Machine learning prediction model for oral mucositis risk in head and neck radiotherapy
T2 - a preliminary study
AU - Kauark-Fontes, Elisa
AU - Araújo, Anna Luiza Damaceno
AU - Andrade, Danilo Oliveira
AU - Faria, Karina Morais
AU - Prado-Ribeiro, Ana Carolina
AU - Laheij, Alexa
AU - Rios, Ricardo Araújo
AU - Ramalho, Luciana Maria Pedreira
AU - Brandão, Thais Bianca
AU - Santos-Silva, Alan Roger
N1 - Publisher Copyright:
© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Head and neck cancer
KW - Oral mucositis
KW - Prediction model
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U2 - 10.1007/s00520-025-09158-6
DO - 10.1007/s00520-025-09158-6
M3 - Article
C2 - 39808310
AN - SCOPUS:85215575598
SN - 0941-4355
VL - 33
SP - 1
EP - 9
JO - Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
JF - Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
IS - 2
M1 - 96
ER -