Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool.
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was...
| Publicado en: | Virchows Archiv: European Journal of Pathology Vol. 475; no. 4; pp. 489 - 498 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139479480&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139479480 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09456317 O1Z jtl: Virchows Archiv: European Journal of Pathology issn: 09456317 maglogo: N pubinfo: dt: Oct2019 vid: 475 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139479480 139479480 NLM31422502 10.1007/s00428-019-02642-5 NLM31422502 139479480 ppf: 489 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. aug: au: Alabi, Rasheed Omobolaji Elmusrati, Mohammed Sawazaki-Calone, Iris Kowalski, Luiz Paulo Haglund, Caj Coletta, Ricardo D. Mäkitie, Antti A. Salo, Tuula Leivo, Ilmo Almangush, Alhadi affil: Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland sug: subj: Neural Networks (Computer) Tongue Neoplasms Pathology Neoplasm Recurrence, Local Sensitivity and Specificity Middle Age Male Internet Aged Prognosis Adult Female Middle Aged: 45-64 years Aged: 65+ years Adult: 19-44 years Male Female ab: Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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