Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Rasheed Omobolaji Alabi; Alhadi Almangush; Mohammed Elmusrati; Antti A Mäkitie

doi:10.3389/froh.2021.794248

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Front Oral Health. 2022 Jan 11:2:794248. doi: 10.3389/froh.2021.794248. eCollection 2021.

Authors

Rasheed Omobolaji Alabi^{1

2}, Alhadi Almangush^{1

3

4}, Mohammed Elmusrati², Antti A Mäkitie^{1

5

6}

Affiliations

¹ Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
² Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
³ Department of Pathology, University of Helsinki, Helsinki, Finland.
⁴ Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.
⁵ Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
⁶ Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden.

Abstract

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

Keywords: deep learning; machine learning; oral cancer; precise surgery; precision medicine; prognostication.