| 1. |
- Bengtsson, Ewert, 1948-, et al.
(författare)
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Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning
- 2018
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Ingår i: CYTO2018.
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Konferensbidrag (refereegranskat)abstract
- Background: The incidence of oral cancer is increasing and it is effecting younger individuals. PAP smear-based screening, visual, and automated, have been used for decades, to successfully decrease the incidence of cervical cancer. Can similar methods be used for oral cancer screening? We have carried out a pilot study using neural networks for classifying cells, both from cervical cancer and oral cancer patients. The results which were reported from a technical point of view at the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), were particularly interesting for the oral cancer cases, and we are currently collecting and analyzing samples from more patients. Methods: Samples were collected with a brush in the oral cavity and smeared on glass slides, stained, and prepared, according to standard PAP procedures. Images from the slides were digitized with a 0.35 micron pixel size, using focus stacks with 15 levels 0.4 micron apart. Between 245 and 2,123 cell nuclei were manually selected for analysis for each of 14 datasets, usually 2 datasets for each of the 6 cases, in total around 15,000 cells. A small region was cropped around each nucleus, and the best 2 adjacent focus layers in each direction were automatically found, thus creating images of 100x100x5 pixels. Nuclei were chosen with an aim to select well preserved free-lying cells, with no effort to specifically select diagnostic cells. We therefore had no ground truth on the cellular level, only on the patient level. Subsets of these images were used for training 2 sets of neural networks, created according to the ResNet and VGG architectures described in literature, to distinguish between cells from healthy persons, and those with precancerous lesions. The datasets were augmented through mirroring and 90 degrees rotations. The resulting networks were used to classify subsets of cells from different persons, than those in the training sets. This was repeated for a total of 5 folds. Results: The results were expressed as the percentage of cell nuclei that the neural networks indicated as positive. The percentage of positive cells from healthy persons was in the range 8% to 38%. The percentage of positive cells collected near the lesions was in the range 31% to 96%. The percentages from the healthy side of the oral cavity of patients with lesions ranged 37% to 89%. For each fold, it was possible to find a threshold for the number of positive cells that would correctly classify all patients as normal or positive, even for the samples taken from the healthy side of the oral cavity. The network based on the ResNet architecture showed slightly better performance than the VGG-based one. Conclusion: Our small pilot study indicates that malignancyassociated changes that can be detected by neural networks may exist among cells in the oral cavity of patients with precancerous lesions. We are currently collecting samples from more patients, and will present those results as well, with our poster at CYTO 2018.
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| 2. |
- Edman, Kristina, 1958-, et al.
(författare)
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Dental hygienists and dentists as providers of brush biopsies for oral mucosa screening
- 2023
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Ingår i: International Journal of Dental Hygiene. - : John Wiley & Sons. - 1601-5029 .- 1601-5037. ; 21:3, s. 524-532
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundOral cancer is a severe and potentially fatal disease usually starting in the squamous epithelium lining the oral cavity. Together with oropharyngeal carcinoma, it is the fifth to sixth most common malignancy worldwide. To limit the increase in the global oral cancer incidence over the past two decades, the World Health Assembly adopted a resolution urging member states to integrate preventive measures such as engagement and training of dental personnel in screening, early diagnosis, and treatment into their national cancer control programs.AimThe aim of this study was to investigate if dental hygienists (DHs) and dentists (Ds) in general dental practice care can be entrusted to perform brush sampling of oral potentially malignant disorders (OPMDs), and to evaluate their level of comfort in performing brush biopsies.MethodsParticipants were five DHs and five Ds who received one day of theoretical and clinical training in oral pathology to identify OPMDs (leukoplakia [LP], erythroplakia [EP], and oral lichen planus [OLP]), and perform brush sampling for PAP cytology and high-risk human papillomavirus (hrHPV) analysis.ResultsOut of 222 collected samples, 215 were adequate for morphological assessment and hrHPV analysis. All the participants agreed that sample collection can be incorporated in DHs and Ds routine clinical duties, and most of them reported that sample collection and processing was easy/quite easy.ConclusionDentists and DHs are capable of collecting satisfactory material for cytology and hrHPV analysis. All the participating DHs and Ds were of the opinion that brush sampling could be handled routinely by DHs and Ds in GDP.
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| 3. |
- Hirsch, JM, et al.
(författare)
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Screening kan minska insjuknade i munhålecancer
- 2017
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Ingår i: Tandläkartidningen. - 0039-6982. ; 2017:9, s. 50-53
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Forskningsöversikt (refereegranskat)abstract
- Oral cancer har hög morbiditet och mortalitet om den inte upptäcks i tid och antalet sjukdomsfall ökar. Ett nationellt program för screening av munhålan skulle kunna minska antalet cancerfall betydligt, eftersom en stor del av cancerfallen orsakas av miljö- och livsstilsfaktorer. Tobak, alkohol och dålig tandhälsa är några av riskfaktorerna.
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| 4. |
- Koriakina, Nadezhda, 1991-, et al.
(författare)
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Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes
- 2019
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Ingår i: 3rd NEUBIAS Conference, Luxembourg, 2-8 February 2019.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: Cancer of the oral cavity is one of the most common malignancies in the world. The incidence of oral cavity and oropharyngeal cancer is increasing among young people. It is noteworthy that the oral cavity can be relatively easily accessed for routine screening tests that could potentially decrease the incidence of oral cancer. Automated deep learning computer aided methods show promising ability for detection of subtle precancerous changes at a very early stage, also when visual examination is less effective. Although the biological nature of these malignancy associated changes is not fully understood, the consistency of morphology and textural changes within a cell dataset could shed light on the premalignant state. In this study, we are aiming to increase understanding of this phenomenon by exploring and visualizing what parts of cell images are considered as most important when trained deep convolutional neural networks (DCNNs) are used to differentiate cytological images into normal and abnormal classes.Materials and methods: Cell samples are collected with a brush at areas of interest in the oral cavity and stained according to standard PAP procedures. Digital images from the slides are acquired with a 0.32 micron pixel size in greyscale format (570 nm bandpass filter). Cell nuclei are manually selected in the images and a small region is cropped around each nucleus resulting in images of 80x80 pixels. Medical knowledge is not used for choosing the cells but they are just randomly selected from the glass; for the learning process we are only providing ground truth on the patient level and not on the cell level. Overall, 10274 images of cell nuclei and the surrounding region are used to train state-of-the-art DCNNs to distinguish between cells from healthy persons and persons with precancerous lesions. Data augmentation through 90 degrees rotations and mirroring is applied to the datasets. Different approaches for class activation mapping and related methods are utilized to determine what image regions and feature maps are responsible for the relevant class differentiation.Results and Discussion:The best performing of the observed deep learning architectures reaches a per cell classification accuracy surpassing 80% on the observed material. Visualizing the class activation maps confirms our expectation that the network is able to learn to focus on specific relevant parts of the sample regions. We compare and evaluate our findings related to detected discriminative regions with the subjective judgements of a trained cytotechnologist. We believe that this effort on improving understanding of decision criteria used by machine and human leads to increased understanding of malignancy associated changes and also improves robustness and reliability of the automated malignancy detection procedure.
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| 5. |
- Lu, Jiahao, et al.
(författare)
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A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
- 2019
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated end-to-end pipeline for oral cancer screening on whole slide cytology images. The pipeline consists of regression based nucleus detection, followed by per cell focus selection, and CNN based classification. We demonstrate that the pipeline provides fast and efficient cancer classification of whole slide cytology images, improving over previous results. The complete source code is made available as open source (https://github.com/MIDA-group/OralScreen).
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| 6. |
- Lu, Jiahao, et al.
(författare)
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A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
- 2020
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Ingår i: Image Analysis and Recognition. - Cham : Springer International Publishing. - 9783030505158 - 9783030505165 ; , s. 249-261
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Konferensbidrag (refereegranskat)abstract
- Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is made available as open source (https://github.com/MIDA-group/OralScreen).
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| 7. |
- Runow Stark, Christina, et al.
(författare)
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Brush Biopsy For HR-HPV Detection With FTA Card And AI For Cytology Analysis - A Viable Non-invasive Alternative
- 2018
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Ingår i: EAOM2018.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Runow Stark, Christina, et al.
(författare)
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Brush Samples of Oral Lesions to FTA Elute Card for High-risk Human Papilloma Virus Diagnosis
- 2021
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Ingår i: Anticancer Research. - : Anticancer Research USA Inc.. - 0250-7005 .- 1791-7530. ; 41:1, s. 269-277
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Tidskriftsartikel (refereegranskat)abstract
- AIM: To investigate the level of agreement between three non-invasive methods for hrHPV diagnosis in oral and oropharyngeal squamous cell carcinoma (OSCC, OPSCC) and in oral mucosal lesions.MATERIALS AND METHODS: For hrHPV DNA FTA Elute card™ and Anyplex II HPV28™ were used and for hrHPV mRNA PreTect SEE™ in tumour patients (n=60), non-tumour lesions (n=51), immunosuppression or previous hrHPV-infection (n=32).RESULTS: The level of agreement between the DNA-methods was 82.2% (k=0.54, p=0.001). Pair-wise comparison for the FTA Elute card were close to the reference (AUC=0.83, 95% CI=0.73-0.90). hrHPV mRNA was diagnosed in 50% of the tumours, with an agreement level of 58.3%, compared to Anyplex II (k=0.17, p=0.04). The hrHPV positivity in oral lesions was 3.9% for immunosuppression and for previous HPV infection 9.4%.CONCLUSION: The FTA card is reliable for hrHPV DNA diagnosis while mRNA gives an insight into viral activity and correlates with severity of the lesion.
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