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Article: Machine Learning and Treatment Outcome Prediction for Oral Cancer

TitleMachine Learning and Treatment Outcome Prediction for Oral Cancer
Authors
Keywordsoral cancer
oral mucosa
Issue Date2020
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0714
Citation
Journal of Oral Pathology & Medicine, 2020, Epub 2020-08-02 How to Cite?
AbstractBackground: The natural history of oral squamous cell carcinoma (OSCC) is complicated by progressive disease including loco‐regional tumour recurrence and development of distant metastases. Accurate prediction of tumour behaviour is crucial in delivering individualized treatment plans and developing optimal patient follow‐up and surveillance strategies. Machine learning algorithms may be employed in oncology research to improve clinical outcome prediction. Methods: Retrospective review of 467 OSCC patients treated over a 19‐year period facilitated construction of a detailed clinicopathological database. 34 prognostic features from the database were used to populate 4 machine learning algorithms, linear regression (LR), decision tree (DT), support vector machine (SVM) and k‐nearest neighbours (KNN) models, to attempt progressive disease outcome prediction. Principal component analysis (PCA) and bivariate analysis were used to reduce data dimensionality and highlight correlated variables. Models were validated for accuracy, sensitivity and specificity, with predictive ability assessed by receiver operating characteristic (ROC) and area under the curve (AUC) calculation. Results: Out of 408 fully characterized OSCC patients, 151 (37%) had died and 131 (32%) exhibited progressive disease at the time of data retrieval. The DT model with 34 prognostic features was most successful in identifying “true positive” progressive disease, achieving 70.59% accuracy (AUC 0.67), 41.98% sensitivity and a high specificity of 84.12%. Conclusion: Machine learning models assist clinicians in accessing digitized health information and appear promising in predicting progressive disease outcomes. The future will see increasing emphasis on the use of artificial intelligence to enhance understanding of aggressive tumour behaviour, recurrence and disease progression.
Persistent Identifierhttp://hdl.handle.net/10722/284787
ISSN
2019 Impact Factor: 2.495
2015 SCImago Journal Rankings: 0.731

 

DC FieldValueLanguage
dc.contributor.authorChu, CS-
dc.contributor.authorLee, NP-
dc.contributor.authorADEOYE, J-
dc.contributor.authorThomson, P-
dc.contributor.authorChoi, SW-
dc.date.accessioned2020-08-07T09:02:37Z-
dc.date.available2020-08-07T09:02:37Z-
dc.date.issued2020-
dc.identifier.citationJournal of Oral Pathology & Medicine, 2020, Epub 2020-08-02-
dc.identifier.issn0904-2512-
dc.identifier.urihttp://hdl.handle.net/10722/284787-
dc.description.abstractBackground: The natural history of oral squamous cell carcinoma (OSCC) is complicated by progressive disease including loco‐regional tumour recurrence and development of distant metastases. Accurate prediction of tumour behaviour is crucial in delivering individualized treatment plans and developing optimal patient follow‐up and surveillance strategies. Machine learning algorithms may be employed in oncology research to improve clinical outcome prediction. Methods: Retrospective review of 467 OSCC patients treated over a 19‐year period facilitated construction of a detailed clinicopathological database. 34 prognostic features from the database were used to populate 4 machine learning algorithms, linear regression (LR), decision tree (DT), support vector machine (SVM) and k‐nearest neighbours (KNN) models, to attempt progressive disease outcome prediction. Principal component analysis (PCA) and bivariate analysis were used to reduce data dimensionality and highlight correlated variables. Models were validated for accuracy, sensitivity and specificity, with predictive ability assessed by receiver operating characteristic (ROC) and area under the curve (AUC) calculation. Results: Out of 408 fully characterized OSCC patients, 151 (37%) had died and 131 (32%) exhibited progressive disease at the time of data retrieval. The DT model with 34 prognostic features was most successful in identifying “true positive” progressive disease, achieving 70.59% accuracy (AUC 0.67), 41.98% sensitivity and a high specificity of 84.12%. Conclusion: Machine learning models assist clinicians in accessing digitized health information and appear promising in predicting progressive disease outcomes. The future will see increasing emphasis on the use of artificial intelligence to enhance understanding of aggressive tumour behaviour, recurrence and disease progression.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0714-
dc.relation.ispartofJournal of Oral Pathology & Medicine-
dc.subjectoral cancer-
dc.subjectoral mucosa-
dc.titleMachine Learning and Treatment Outcome Prediction for Oral Cancer-
dc.typeArticle-
dc.identifier.emailThomson, P: thomsonp@hku.hk-
dc.identifier.emailChoi, SW: htswchoi@hku.hk-
dc.identifier.authorityThomson, P=rp02327-
dc.identifier.authorityChoi, SW=rp02552-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/jop.13089-
dc.identifier.pmid32740951-
dc.identifier.scopuseid_2-s2.0-85089596944-
dc.identifier.hkuros312460-
dc.identifier.volumeEpub 2020-08-02-
dc.publisher.placeDenmark-

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