Impact of standardised reporting in adrenocortical carcinoma: a single centre clinicopathological review
Bibliographic record
Abstract
AIMS: Structured multicentre efforts are needed if the prognosis of adrenocortical carcinoma (ACC) is to be improved. Data collection may be enhanced through standardised histopathological reporting using criteria such as the recently published Royal College of Pathologists' (UK) minimum dataset (MDS). This study aimed to perform a clinicopathological review of the adult patients treated at the Royal Victoria Infirmary, Newcastle upon Tyne, in the 10 years preceding the MDS. METHODS: Case records were examined for all patients diagnosed with ACC between 1996 and 2006. Pathology was reviewed and compared with the Royal College of Pathologists' MDS along with the original reports. A systematic evaluation of Ki-67 immunolabelling was also performed. RESULTS: Eleven patients with ACC were diagnosed and treated. Histopathological reporting according to the MDS identified more features of malignancy than in the original reports (8.5+/-1.2 versus 5.1+/-0.8, p<0.02). The median number of microscopic criteria of malignancy was 7 (range 5-10), with > or =5 features occurring in all cases. The most commonly observed features of malignancy were diffuse architecture, <25% clear cells, confluent necrosis, abnormal mitoses and mitotic count > or =6 per 50 high-power fields. Capsular invasion and > or =8 MDS criteria of malignancy were associated with a worse outcome (each p<0.01). Median Ki-67 index was 19.0% (range 3.7-44.1%) and was not apparently related to survival. CONCLUSIONS: Standardised criteria for histopathological reporting of ACC will improve the accuracy of data for cancer registration and may also assist in individual patient stratification. An elevated Ki-67 index is a feature of ACC, although it does not appear to predict individual patient survival.
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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 | 0.033 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".