Open this publication in new window or tab >>Show others...
2018 (English)In: EAOM2018 / [ed] Bengt Hasséus, 2018Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]
Introduction: Oral cancer accounts for about 800-1,000 new cases each year in Sweden and the ratio of cancer related to high-risk human papillomavirus (HR-HPV) is increasing in the younger population due to changes in sexual habits. The most two frequent HR-HPV types 16 and 18 have both significant oncogenic potential.
Objectives: In this pilot study we evaluate two non-invasive automated methods; 1) detection of HR-HPV using FTA cards, and 2) image scanning of cytology for detection of premalignant lesions as well as eradicate the early stage of neoplasia.
Material and Methods: 160 patients with verified HR-HPV oropharyngeal cancer, previous ano-genital HR-HPV-infection or potentially malignant oral disorder were recruited for non-invasive brush sampling and analyzed with two validated automated methods both used in cervix cancer screening. For analysis of HR-HPV DNA the indicating FTA elute micro cardTM were used for dry collection, transportation and storage of the brush samples. For analysis of cell morphology changes an automated liquid base Cytology method (Preserve Cyt) combined with deep learning computer aided technique was used.
Results: Preliminary results show that the FTA-method is reliable and indicates that healthy and malignant brush samples can be separated by image analysis.
Conclusions: With further development of these fully automated methods, it is possible to implement a National Screening Program of the oral mucosa, and thereby select patients for further investigation in order to find lesions with potential malignancy in an early stage.
Keywords
cytometry, deep learning, oral cancer, image analysis, HPV
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367985 (URN)
Conference
14th Biennial Congress of the European Association of Oral Medicine, Göteborg, Sweden, September 2018
Funder
Vinnova, 2017-02447
2018-12-022018-12-022021-11-25Bibliographically approved