University of Oulu

Alabi, R.O., Elmusrati, M., Sawazaki-Calone, I. et al. Virchows Arch (2019) 475: 489.

Machine learning application for prediction of locoregional recurrences in early oral tongue cancer : a Web-based prognostic tool

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Author: Alabi, Rasheed Omobolaji1; Elmusrati, Mohammed1; Sawazaki-Calone, Iris2;
Organizations: 1Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
2Oral Pathology and Oral Medicine, Dentistry School, Western Parana State University, Cascavel, PR, Brazil
3Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, SP, Brazil
4Research Programs Unit, Translational Cancer Biology, University of Helsinki, Helsinki, Finland
5Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
6Department of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, Brazil
7Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
8Research Programme in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
9Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
10Department of Pathology, University of Helsinki, Helsinki, Finland
11Department of Oral and Maxillofacial Diseases, University of Helsinki, Helsinki, Finland
12Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
13Institute of Biomedicine, Pathology, University of Turku,Turku, Finland
14Faculty of Dentistry, University of Misurata, Misurata, Libya
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Springer Nature, 2019
Publish Date: 2019-12-02


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.

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Series: Virchows Archiv. European journal of pathology
ISSN: 0945-6317
ISSN-E: 1432-2307
ISSN-L: 0945-6317
Volume: 475
Issue: 4
Pages: 489 - 497
DOI: 10.1007/s00428-019-02642-5
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
Funding: Open access funding provided by University of Turku (UTU) including Turku University Central Hospital. This work was supported by the Finnish Dental Society, the Rauha Ahokas Foundation, the K. Albin Johanssons Foundation, Turku University Hospital Fund, Helsinki University Hospital Research Fund, the Finnish Cancer Society, Finska Läkaresällskapet, the Maritza and Reino Salonen Foundation, and the UOPECCAN Center of Study and Research.
Copyright information: © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.