Diagnostic utility of androgen receptor expression in discriminating poorly differentiated urothelial and prostate carcinoma
Bibliographic record
Abstract
AIMS: Pathological separation of poorly differentiated urothelial and prostate carcinoma is difficult, but imperative because of the impact on patient management. Tumour morphology, in conjunction with a panel of immunohistochemistry (IHC), such as prostate-specific antigen (PSA), prostatic acid phosphatase (PSAP), CK7, CK20, p63 and high molecular weight keratins (HMWKs) are usually employed to resolve this issue. Androgen receptor (AR) expression is maintained in high-grade, undifferentiated prostate carcinoma, and thus, could be considered as a potentially useful adjunct to the conventional panel of markers. METHODS: We performed an institutional review of all cases from 2006 to 2012 in which AR IHC had been performed to determine its diagnostic utility in discriminating between poorly differentiated urothelial and prostate carcinoma. Of the eligible cases (n=40), there were 9 high-grade urothelial carcinomas, 27 prostate carcinomas and 4 with both prostate and bladder tumours. All diagnoses were made by integrating the clinical, radiological, morphological and IHC results. RESULTS: In all the prostate carcinomas, there was diffuse, intense nuclear staining for AR. The urothelial tumours were either negative, had cytoplasmic staining or showed occasionally weak nuclear staining. The difference was highly significant with p<0.0001 (Mann-Whitney U test). CONCLUSIONS: We conclude that AR is an important marker as it is best able to distinguish between poorly differentiated urothelial and prostate carcinoma. AR appears superior to PSA and PSAP, which are not consistently expressed in high-grade prostate carcinoma. Also, high-grade urothelial carcinoma may be negative for CK20, p63/HMWK and occasionally CK7. We advocate the inclusion of AR in the panel of markers to differentiate these tumours.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".