Assessment of CK17 as a Marker for the Diagnosis of Differentiated Vulvar Intraepithelial Neoplasia
Bibliographic record
Abstract
Differentiated vulvar intraepithelial neoplasia (dVIN), precursor of vulvar squamous cell carcinoma, is human papilloma virus independent and often found in a background of lichen sclerosus (LS) and lichen simplex chronicus (LSC). Subtle histologic findings make the diagnosis of dVIN difficult, and, although the use of p53 and Ki-67 has been of some value, there is a need for a better immunohistochemical marker. Cytokeratin 17 (CK17), a cytoskeletal intermediate filament protein, has previously been used in the diagnosis of anogenital lesions. Here we evaluated CK17 in dVIN in comparison with LS, LSC, and usual VIN (uVIN/HSIL). Twenty-nine cases of dVIN, 9 cases of uVIN, 8 cases of LS, and 7 of LSC were evaluated using CK17, Ki-67, and p53. All 29 dVIN cases displayed immunoreactivity for CK17, with 27 (93%) showing intermediate to strong and diffuse reactivity. No cases of uVIN displayed diffuse CK17 expression, whereas 63% of LS and 29% of LSC displayed intermediate to strong diffuse immunoreactivity, confined to the upper half of the epithelium. P53 and Ki-67 expression was present in varying degrees in all types of lesions, displaying limited discriminatory power for dVIN. Our findings suggest that CK17, although not specific for dVIN, when combined with histologic findings, Ki-67, and p53 immunohistochemistry, can be a marker of vulvar dysplasia and serve as an adjunct in the diagnosis of dVIN. Specifically, in small biopsies, the presence of diffuse suprabasal or full thickness expression strongly favors a diagnosis of dVIN over LSC, whereas focal and/or superficial expression supports a diagnosis of LSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".