Diagnostic and prognostic features in adrenocortical carcinoma: a single institution case series and review of the literature
Bibliographic record
Abstract
BACKGROUND: Adrenocortical carcinoma is a rare cancer, with an incidence in the literature of 0.5 to 2 cases per million population per year. Adult adrenocortical carcinoma has a poor prognosis, underscoring the importance of identifying diagnostic and prognostic markers. METHODS: We searched our laboratory database for all cases in the past 15 years with a diagnosis of adrenocortical carcinoma. The original slides were then reviewed for their histopathological features. A representative paraffin block was subjected to further immunohistochemical staining for Ki-67, inhibin, steroidogenic factor-1 (SF-1), p53, and Β-catenin. These slides were scored by the study pathologist who was blinded to all clinicopathological data. In addition, a comprehensive review of the relevant English literature in the past 15 years was conducted. RESULTS: Eight cases were identified, including two adrenal sarcomatoid carcinomas. Seven of the eight cases had a disrupted reticulin network. Six of the eight tumors had >10% Ki-67 expression. Five of the eight tumors had >10% p53 expression. Positive inhibin immunohistochemical staining was seen in three of the eight tumors, and positive SF-1 staining was seen in five of the seven stained tumors. Abnormal Β-catenin intracellular accumulation was noted in four of the eight tumors. The two tumors in our series with sarcomatoid histology did not stain positively for SF-1 or inhibin. CONCLUSIONS: Eight cases of adrenocortical carcinoma, including two with sarcomatoid features are presented. The two sarcomatoid adrenocortical carcinomas in our series did not stain for SF-1 which suggests a possible de novo pathway of tumorigenesis for this rare variant. The reticulin staining method was a useful tool for rapid differentiation of adrenocortical adenomas and carcinomas. Diffuse p53 staining showed a trend for positive correlation with increased Ki-67 expression. Inhibin staining was inconsistently expressed in our cases of adrenocortical carcinoma. In conclusion, as adrenocortical carcinoma is a rare disease, we recommend future multicenter studies with appropriate sample sizes to further evaluate the efficacy of these diagnostic and prognostic markers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".