Role of Immunohistochemistry in the Diagnosis of Solitary Fibrous Tumor, a Review.
Bibliographic record
Abstract
BACKGROUND: Solitary fibrous tumor (SFT) is a mesenchymal tumor which is most commonly seen in the pleura; however it can be seen in other organs such as the meninge, gastrointestinal tract, soft tissue, bone, and skin. SFT should be differentiated from other mesenchymal tumors in these organs. Immunohistochemistry plays a pivotal role for the histopathologic diagnosis of this tumor. Currently, new markers have been introduced which has been very useful for definite diagnosis of SFT along with other markers in each specific location which are negative in SFT. METHODS: Here we review the reported positive and negative immunohistochemical markers of SFT in the English literature with the emphasis on the useful markers in each specific organ. We explored the English literature from 1990 through 2015 via PubMed, Google, and Google scholar using the following search keywords: Solitary fibrous tumor, Solitary fibrous tumor and immunohistochemistry, Solitary fibrous tumor and diagnosis, Solitary fibrous tumor and histogenesis, Solitary fibrous tumor and prognosis, Solitary fibrous tumor and hemangiopericytoma, Solitary fibrous tumor and differential diagnosis, Solitary fibrous tumor and markers. RESULTS: The most important and valuable positive markers in SFT are CD34, CD99, Bcl-2 and STAT-6.There are consistently negative markers in this tumor as well, used according to the tumor location, such as EMA and S100. CONCLUSION: Immunohistochemistry is very useful for the diagnosis of solitary fibrous tumor and for its differentiation with other spindle cell mesenchymal tumor in different locations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".