A Clinicopathological Analysis of Asian Patients with Adrenocortical Carcinoma: A Single-Center Experience
Bibliographic record
Abstract
Background: There is limited information regarding the immunohistochemistry stain and its prognostic role in adrenocortical carcinoma (ACC), and few studies focus on Asian patients. Our study aims to identify the correlation between immunohistochemistry staining and the prognosis of ACC in Asian patients. Methods: We searched the database of a single center in Taiwan for cases with a pathological diagnosis of ACC in the past 25 years. We collected patient data on age, sex, initial presentation, staging, metastatic site, and survival duration. Immunohistochemical studies using antibodies to CDK4, ATRX, beta-catenin, Ki-67, SSTR2, and p53 were performed. Survival analysis was performed using the log-rank test, the Cox proportional hazards model and bootstrapping with 5000 samplings. Results: Fourteen patients were identified, and the median age was 49.5 (range 1–70) years. There were eight male and six female patients. Four patients presented with Cushing’s syndrome, and half were diagnosed with stage IV ACC at presentation. Only three patients survived (21%). The median survival time was 15.5 (range 0.67–244) months. SSTR2 expression score > 50 (log-rank test: p = 0.009) and Ki-67 > 50% (log-rank test: p = 0.017) were associated with mortality. However, after adjusting for stage, the bootstrapping analysis demonstrated that Ki-67 [B 2.04, p = 0.004], Beta-catenin [B 2.19, p = 0.009], ATRX [B 1.48, p = 0.026], P53 [B 1.58, p = 0.027], SSTR2 [B 1.58, p = 0.015] and SSTR2 expression score [B 0.03, p < 0.001] were all significantly associated with mortality. Conclusions: After adjusting for stage, Ki-67 > 50%, Beta-catenin, ATRX, P53, SSTR2 and SSTR2 expression score > 50 were associated with mortality in Asian patients with ACC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".