Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.
| Title: | Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study. |
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| 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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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kauark-Fontes%2C+Elisa%22">Kauark-Fontes, Elisa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> elisakauark@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Araújo%2C+Anna+Luiza+Damaceno%22">Araújo, Anna Luiza Damaceno</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Andrade%2C+Danilo+Oliveira%22">Andrade, Danilo Oliveira</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Faria%2C+Karina+Morais%22">Faria, Karina Morais</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Prado-Ribeiro%2C+Ana+Carolina%22">Prado-Ribeiro, Ana Carolina</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Laheij%2C+Alexa%22">Laheij, Alexa</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rios%2C+Ricardo+Araújo%22">Rios, Ricardo Araújo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramalho%2C+Luciana+Maria+Pedreira%22">Ramalho, Luciana Maria Pedreira</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brandão%2C+Thais+Bianca%22">Brandão, Thais Bianca</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Santos-Silva%2C+Alan+Roger%22">Santos-Silva, Alan Roger</searchLink><relatesTo>7</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Supportive+Care+in+Cancer%22">Supportive Care in Cancer</searchLink>. Feb2025, Vol. 33 Issue 2, p1-9. 9p. – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Supportive Care in Cancer is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00520-025-09158-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Titles: – TitleFull: Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kauark-Fontes, Elisa – PersonEntity: Name: NameFull: Araújo, Anna Luiza Damaceno – PersonEntity: Name: NameFull: Andrade, Danilo Oliveira – PersonEntity: Name: NameFull: Faria, Karina Morais – PersonEntity: Name: NameFull: Prado-Ribeiro, Ana Carolina – PersonEntity: Name: NameFull: Laheij, Alexa – PersonEntity: Name: NameFull: Rios, Ricardo Araújo – PersonEntity: Name: NameFull: Ramalho, Luciana Maria Pedreira – PersonEntity: Name: NameFull: Brandão, Thais Bianca – PersonEntity: Name: NameFull: Santos-Silva, Alan Roger IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09414355 Numbering: – Type: volume Value: 33 – Type: issue Value: 2 Titles: – TitleFull: Supportive Care in Cancer Type: main |
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