Abstract A17: Using high-throughput imaging cytometry to unmask the true nature of oral lesions in a high-risk community
Bibliographic record
Abstract
Abstract Vancouver's Downtown Eastside is a high-risk community for oral cancer. Previous study has shown its prevalence of tobacco and alcohol consumption and high incidence of oral cancer (one cancer identified in 150 residents screened). Additionally, oral infection and inflammation are commonly seen in this community. In British Columbia, we are evaluating several technologies and their interactions for the screening and early detection. Direct fluorescence visualization (FV) has shown its high sensitivity for the detection of oral cancer or precancer, but can also highlight other oral conditions which are commonly seen within community setting. We start to look at the utility of imaging cytometry as an adjunct tool in oral cancer screening. There is an urgent need to develop an effective strategy for oral cancer screening in such a high risk community. Objectives: 1) To using imaging cytometry to measure the altered nuclear phenotype (cNPS) and 2) To see if cNPS can increase the accuracy of using FV only for oral screening. Methods: From 2004/11–2009/2, we have collected 355 exfoliative cell samples using a small curved interdental tooth brush from this high-risk community. Samples were spun down onto slides, the DNA quantitatively labeled and automatically scanned by the cyto-savant®. For each object (nucleus/debris) imaged ∼110 features were calculated and used by a cell recognition decision tree to differentiate cells from debris. Results: Among 355 brushings, 4 from cancerous sites, 90 from non cancerous common oral lesions (60, trauma; 28, inflammation; 12, infection), and from tongue with no lesion under clinical white light and FV examinations. Using previously trained algorithm with 84% sensitivity and 97% specificity to examine these samples, there was no difference between gender, age groups, smoking habit, and immune status (presence of HIV infection). This algorithm can correctly identified 3 out of 4 high-grade lesions and 89% normal cases. For those non-cancerous common oral lesions, using FV followed with the examination of cNPS at the FV loss area can drastically increase the accuracy from 10% to 83% (trauma, 13% to 90%; inflammation, 6% to 78%; infection 0% to 75% respectively). Conclusion: The pilot results indicate support the potential usage of the combination of direct FV and imaging cytometry in oral cancer screening in a high-risk community. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A17.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".