| Texto completo | |
| Autor(es): |
Kauark-Fontes, Elisa
;
Araujo, Anna Luiza Damaceno
;
Andrade, Danilo Oliveira
;
Faria, Karina Morais
;
Prado-Ribeiro, Ana Carolina
;
Laheij, Alexa
;
Rios, Ricardo Araujo
;
Ramalho, Luciana Maria Pedreira
;
Brandao, Thais Bianca
;
Santos-Silva, Alan Roger
Número total de Autores: 10
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SUPPORTIVE CARE IN CANCER; v. 33, n. 2, p. 9-pg., 2025-02-01. |
| Resumo | |
PurposeOral 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.MethodsClinical 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.ResultsA 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.ConclusionML 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. (AU) | |
| Processo FAPESP: | 18/02233-6 - Segurança oncológica no uso da fotobiomodulação intraoral e extraoral em pacientes com carcinoma espinocelular de cavidade oral e orofaringe. |
| Beneficiário: | Alan Roger dos Santos Silva |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 18/23479-3 - Segurança oncológica no uso da fotobiomodulação extraoral em pacientes com carcinoma espinocelular de cavidade oral e orofaringe |
| Beneficiário: | Elisa Kauark Fontes |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |