MDM2 and CDK4 Immunostainings Are Useful Adjuncts in Diagnosing Well-Differentiated and Dedifferentiated Liposarcoma Subtypes
Bibliographic record
Abstract
Atypical lipomatous tumor/well-differentiated liposarcoma (ALT-WDLPS) and dedifferentiated liposarcoma (DDLPS) may be difficult to distinguish from benign adipose tumors and from poorly differentiated sarcomas, respectively. Genetically, they are characterized by amplification of MDM2 and CDK4 genes on chromosome 12q13-15. We examined a series of 559 soft tissue tumors (44 ALT-WDLPS, 61 DDLPS, 49 benign adipose tumors, and 405 non-ALT-WDLPS/DDLPS sarcomas) for MDM2 and CDK4 expression using immunohistochemistry. MDM2 and CDK4 immunoexpressions were compared with gene amplification status (as assessed by quantitative PCR and/or comparative genomic hybridization) in 241 neoplasms. Most ALT-WDLPS/DDLPS expressed MDM2 (97%) and CDK4 (92%) as opposed to few benign adipose tumors (MDM2, 5%; CDK4, 2%) and a limited number of non-ALT-WDLSP/DDLPS sarcomas (MDM2, 19%; CDK4, 6%). The sensitivity and specificity of MDM2 and CDK4 immunostainings in identifying ALT-WDLPS/DDLPS among other soft tissue tumors were 97% and 92%, and 83% and 95%, respectively. MDM2 and CDK4 immunostainings were particularly useful to separate ALT-WDLPS from the large group of differentiated adipose tumors, and to distinguish DDLPS from poorly differentiated sarcomas. A strong correlation was observed between MDM2 and CDK4 stainings and gene amplification status. In conclusion, MDM2 and CDK4 immunostainings, which correlate with gene amplification, are helpful adjuncts to differentiate ALT-WDLPS from benign adipose tumors and to separate DDLPS from poorly differentiated sarcomas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".