Combination of MCM2 With Ki67 and p16 Immunohistochemistry Can Distinguish Uterine Leiomyosarcomas
Bibliographic record
Abstract
Our objective was to evaluate the diagnostic utility of 2 new proliferation markers, cyclin D1 and minichromosome maintenance complex component 2 (MCM2), in comparison with p16, p53, and Ki67 in differentiating the spectrum of smooth muscle tumors. An institutional database search from 2009 to 2017 identified 10 cases of uterine leiomyoma with bizarre nuclei (LBN), 12 smooth muscle tumors of uncertain malignant potential, and 13 leiomyosarcomas (LMS). Ten resected leiomyomas (LM) were included as controls. Immunohistochemistry was performed on the befitting representative block from each case. Ki67 was <10% in all LMs and LBNs, whereas >10% in all LMSs. Although wild-type in majority of cases, p53 was overexpressed in 38% of LMSs. Cyclin D1 nuclear positivity in LMs, LBNs, and smooth muscle tumors of uncertain malignant potentials ranged from 0% to 65% of neoplastic cells with mostly weak to moderate staining intensity. Instead, cyclin D1 expression was <5% in all LMSs. The ratio of MCM2 positivity exhibited a similar wide range (<1%-80%) in LMs, LBNs, and smooth muscle tumors of uncertain malignant potentials but interestingly, 92% (12/13) of LMSs were diffusely and strongly positive for MCM2 (>80% cell positivity). Overall, for diagnosis of LMS, the sensitivity for diffuse intense MCM2 staining was higher (92%) compared with diffuse staining for p16 (77%); however, specificity of MCM2 and p16 was comparable (94% and 97%, respectively). Herein, we describe the immunohistochemical profile of 2 new proliferation markers, cyclin D1 and MCM2 in uterine smooth muscle tumors. A combination of diffuse strong MCM2 and p16 reactivity with increased Ki67 index can reliably distinguish LMSs from benign histologic mimics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".