Application of microscope‐based scanning software (Panoptiq) for the interpretation of cervicovaginal cytology specimens
Bibliographic record
Abstract
BACKGROUND: Digital pathology increasingly has been gaining the attention of pathologists worldwide. However, the application of digital cytology by Panoptiq (ViewsIQ, Vancouver, Canada) microscope-based scanning software is relatively unexplored. Panoptiq enables the operator to combine low-power panoramic digital images with z-stacks at regions of interest with a significantly smaller image size than that obtained by whole-slide scanning. The current study aimed to evaluate the feasibility of the use of Panoptiq in the digital interpretation of cervicovaginal cytology specimens in comparison with conventional light microscopy. METHODS: A total of 100 liquid-based cytology slides were selected sequentially. The dotted slides were reviewed and scanned, in which all dotted areas were scanned further by the ×20 objective with z-stacks. The cases were reviewed by 4 pathologists and a cytotechnologist using conventional light microscopy and digital cytology images acquired by Panoptiq and interpreted based on the Bethesda classification system. The washout time was set as 3 weeks. The Cohen kappa coefficient was calculated to measure the agreement between the 2 modalities. RESULTS: Digital cytology demonstrated an intermodality agreement among 3 observers who had sufficient training in digital pathology at concordance rates between 81% and 90% with kappa values between 0.76 and 0.86, whereas the other 2 observers who did not have sufficient training in digital pathology had lower agreement at a concordance rate of between 56% and 57%, with kappa values between 0.41 and 0.44. CONCLUSIONS: Panoptiq appears to be feasible for the interpretation of cervicovaginal cytology specimens but requires adequate training in digital pathology. Cancer Cytopathol 2017;125:918-25. © 2017 American Cancer Society.
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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 | 0.001 | 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".