Imaging and machine learning methods for assessing HPV in situ hybridisation patterns in oropharyngeal carcinomas

Fouad, Shereen and Landini, Gabrial and Robinson, Max and Mehann, Hisham and Randell, David A (2018) Imaging and machine learning methods for assessing HPV in situ hybridisation patterns in oropharyngeal carcinomas. In: 14th European Congress on Digital Pathology, 29th May - 1st June, 2018, Finland.

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Some Human Papilloma Virus (HPV) strains are considered aetiological factors in oropharyngeal carcinomas. Interestingly, HPV+ neoplasms appear to have a different demographic and relatively better prognosis than HPV- ones, making HPV status a relevant diagnostic and prognostic feature. HPV status assessed by in situ hybridisation is particularly challenging due to the complexity of staining patterns, high resolution imaging requirements and presence of staining artefacts, all of which call into question the feasibility of imaging techniques for analysing such histological samples. We present a machine learning-based, automated imaging workflow for the identification of HPV status in digitized tissue microarray samples of oropharyngeal carcinomas. High-risk HPV genomes assessed with the INFORM HPV-III system (Ventana) revealed blue stained regions (NBT/BCIP) in epithelial nuclei on a pink background (Red Counterstain II). The contributions of the two dyes (plus a residual colour component) were determined using colour deconvolution. Blue regions were segmented using Renyi's entropy thesholding, while stain artefacts were identified by feature co-occurrence in the blue and residual channels. Morphological parameters of the segmented regions and clinical data were summarized and submitted to a set of supervised recognition classifiers. The evaluation of 695 cases (2085 TMA images, x20 magnification) using feature selection procedures in conjunction with a support vector machine classifier achieved an average of 90% accuracy in detecting HPV status when compared with the histopatholgist scoring as standard. In conclusion, imaging of in situ hybridisation patterns can provide automated means of screening HPV status in large datasets at known levels of accuracy.

Item Type: Conference or Workshop Item (Lecture)
29 May 2018Published
29 March 2018Accepted
Subjects: CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-01 - medical technology
CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Depositing User: Shereen Fouad
Date Deposited: 02 Apr 2019 09:15
Last Modified: 12 Jan 2022 12:53

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