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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".