Best Practices Recommendations in the Application of Immunohistochemistry in Testicular Tumors
Bibliographic record
Abstract
The judicious use of immunostains can be of significant diagnostic assistance in the interpretation of testicular neoplasms when the light microscopic features are ambiguous. A limited differential diagnosis by traditional morphology is required for the effective use of immunohistochemistry (IHC); otherwise, the inevitable occurrence of exceptions to anticipated patterns will lead to "immunoconfusion." The diagnosis of tumors in the germ cell lineage, the great majority of primary tumors of the testis, has been considerably facilitated over the past decade by IHC directed at developmentally important nuclear transcription factors, including OCT4, SALL4, SOX2, and SOX17, that are mostly restricted to certain tumor histotypes. In conjunction with other markers, a specific diagnosis can be achieved in most instances through a panel of 3 or 4 immunostains and often fewer. IHC among tumors in the sex cord-stromal group may produce a significant proportion of false-negative cases until more sensitive and equally specific markers are validated. The negativity of these tumors for the IHC stains used for germ cell tumors is key in the important distinction of neoplasms in these 2 general categories. In this review, the International Society of Urological Pathologists (ISUP) provides diagnostic guidelines in the form of algorithms to assist practicing pathologists confronting a differential diagnostic question concerning a testicular neoplasm. The goal of ISUP is to anticipate commonly encountered differential diagnoses and recommend an efficient and limited pattern of IHC stains to resolve the question.
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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 | 0.025 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".