High‐resolution image cytometry on smears of normal oral mucosa: a possible approach for the early detection of laryngopharyngeal cancers
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate the possibility of identifying laryngopharyngeal cancers by nuclear chromatin texture feature analysis of cell nuclei from mucosal scrapings obtained from clinically and cytologically noncancerous areas of the soft palate in patients with cancer. METHODS: The collective consisted of 68 controls and 77 cases of laryngopharyngeal carcinomas. After Feulgen staining, 3000 cell nuclei were automatically measured using a high-resolution image analyser (CytoSavant Oncometrics, Vancouver, BC, Canada). Texture features were extracted for calculation of a discriminant function, which allows the two groups to be distinguished. RESULTS: Two parameters allowed the two populations to be distinguished. The classifier reached an overall performance of 72.7% sensitivity, 82.4% specificity, a positive predictive value of 80.5%, a negative predictive value of 75.1%, and an area under the receiver operating characteristics (ROC) curve of 0.7754. CONCLUSION: Our work shows that subtle changes in the chromatin distribution in cell nuclei from ostensibly normal cells in the vicinity of carcinomas are demonstrable in the oral cavity of patients suffering from laryngopharyngeal cancers. It may be possible to develop this method into a valuable clinical tool to reduce the high rate of delayed diagnosis of oral and laryngopharyngeal cancers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".