Martial Guillaud

It is believed the majority of oral cancers are preceded by an oral potentially malignant lesion (OPML). Histopathological diagnosis of an OPML is required to determine the presence of dysplasia. However, it is challenging for clinicians to identify which lesions to biopsy. The increasing degree of dysplasia is associated with increasing risk of malignant transformation. Although low-grade dysplasias make up the majority of the dysplasias diagnosed, it is difficult to predict their individual risk of malignant progression. DNA ploidy, measured using DNA image cytometry (DNA-ICM), has shown to be a biomarker for dysplasia and malignancy in various sites including the oral cavity. In this thesis, three research projects were developed to explore the role of DNA-ICM as a triage and screening tool for oral LGD. The first project revealed that the combination of DNA ploidy and chromatin organization measurement using LGD lesion brushings was a strong predictor of progression. Further, temporal assessment of ploidy helped us identify risk patterns of progression among oral LGDs, which can be useful for clinicians to determine triage and management of LGDs. The second project aimed to conduct oral cancer screening in a high-risk population in British Columbia (BC) and assess the use of DNA-ICM and fluorescent visualization (FV) as adjunct screening tools. Our results confirmed previous findings, where South Asian immigrants in BC showed a higher prevalence of low-grade dysplasia than the general Vancouver population. No abnormal DNA-ICM findings were noted in the small number of dysplasias detected. The final project aimed to assess the effectiveness of DNA-ICM combined with cytology and FV as an adjunct screening tool for community oral cancer screening in rural India. Results showed that DNA-ICM along with cytology showed satisfactory sensitivity and specificity in detecting dysplasias and identified additional lesions that required biopsy. This work shows that DNA-ICM when combined with cytology can serve as a non-invasive, cost-effective screening tool and aid clinicians to triage LGDs to detect progression early. This work also helps gain understanding on the role of DNA aneuploidy in oral cancer.

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Two directions are explored for improving the current cervical cancer diagnosis procedure. The first investigates the future deployment of in vivo confocal imaging in the clinic, for detecting precancerous tissues, and the second proposes an algorithm for automatic interpretation of histology images (acquired by light microscopy). We acquired i) confocal microscopy images of cervical biopsies taken from 50 patients, at different tissue depths and ii) histology images of different sections cut from each biopsy. From the confocal images, we identified four features that carry enough information relevant to cell morphology and tissue architecture. We demonstrated that the relevant information in these features is comparable to that extracted from the same features in histology images. This implies that we can obtain the relevant information from confocal imaging, without having to cut a biopsy from the patient’s cervix. We then studied the confocal images and determined the grade lesion of every biopsy and found that confocal imaging resulted in less false positives than the diagnosis given by the gynecologist (based on the appearance of the cervix under colposcopy). Utilizing confocal microscopy technology in the clinic would thus decrease the number of unnecessary biopsies. We then developed a deep learning algorithm that automatically and quantitatively assesses HPV contaminated and proliferating cells in histology images of biopsy sections. The automatic assessment of this procedure is important as it plays a significant role in differentiating between disease grades but forms a challenging and complex task and demands a large amount of time when performed manually by a pathologist. We demonstrated that this algorithm could help the pathologists to differentiate between different grades of cervical precancerous tissues. Our results are also more reproducible compared to other methods (like color deconvolution) that are widely being used in the field of digital pathology. The in vivo imaging and automatic image analysis algorithms demonstrated in the thesis can potentially enable i) real time diagnosis in the clinic, and ii) fast interpretation of histology images in a reproducible and cost-effective manner. While developed for cervical neoplasia, these methods could be extended to oral cavity, skin, and other epithelial tissue cancers.

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