Detection of Cervical Cancer and High Grade Neoplastic Lesions by a Combination of Liquid‐Based Sampling Preparation and DNA Measurements Using Automated Image Cytometry
Bibliographic record
Abstract
OBJECTIVE: To establish if measurements of DNA ploidy could be used to assist cytopathologists and cytotechnologists in population based cervical cancer screening programs in countries where manually reading the slides is impossible due to the lack of sufficient skilled cytotechnologists. The goal of such program is to identify only clinically significant lesions, i.e. those where a clinical intervention to remove the lesion is required immediately. STUDY DESIGN: A total of 9905 women were enrolled in the study. Cervical samples were taken with a cervix brush that was then placed into a fixative solution. The cells were separated from mucus by mechanical and chemical treatment and then deposited onto microscope slides by a cytocentrifuge. Two slides were prepared from each case; one slide was stained by Papanicolaou stain for manual cytology examination, while the other slide was stained by a DNA specific stain. The latter slide was used to determine the relative amount of DNA in the cell nuclei. RESULTS: A total of 876 women were followed by colposcopy examination where biopsies were taken from the visible lesions or from suspicious areas and histopathology diagnosed 459 as normal or benign cases, 325 as CIN1, 36 as CIN2, 25 as CIN3/CIS, and 31 as invasive cancer. Of these 876 cases, manual cytology called 655 normal or ASCUS, 197 as LSIL, 16 cases as HSIL, and 8 as cancer. DNA measurements found 704 cases having no cells with DNA greater than 5c, 98 cases where there were 1 or 2 cells having DNA amount greater than 5c, and 74 cases where there were 3 or more cells having DNA amount greater than 5c. If manual cytology were to be used to refer all cases of HSIL and cancer to colposcopy and biopsy, 23 lesions that had to be removed would have been discovered (2 CIN2, 11 CIN3/CIS, and 10 cancers), for a sensitivity of 25.0+/-5.2% at specificity of 99.9+/-0.1%. If DNA assisted cytology were to be used instead, and all cases having 3 or more cells with DNA amount greater than 5c were to be referred to colposcopy and biopsy, then 50 lesions that had to be removed would have been discovered (10 CIN2, 15 CIN3/CIS and 25 cancers) for the sensitivity of 54.3+/-6.2% at specificity of 96.9+/-0.6%. CONCLUSIONS: The study suggests that screening for high grade cervical neoplastic lesions and cervical cancer by DNA assisted cytology could be implemented with minimal use of skilled cytotechnologists, at least in those countries where it would be difficult to introduce population based screening for cervical cancer due to the lack of availability of such skilled cytotechnologists.
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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".