Adrenal Incidentaloma: A Cautionary Tale of Three Cases of Adrenocortical Carcinoma Arising from Apparently Benign Incidentalomas
Bibliographic record
Abstract
ABSTRACT Unexpected incidental findings on cross-sectional imaging are becoming more commonplace in today's medical practice. These are likely due to ongoing improvements in the resolution of cross-sectional imaging and our increasing use of these tests combined with an aging population. In the case of the adrenal incidentalomas the majority of these represent benign nonfunctional adenomas and these are believed to have no malignant potential. On the contrary adrenocortical carcinoma (ACC) is an uncommon malignancy that carries a high mortality. Current biochemical and radiological follow-up investigations are expensive and are of limited benefit in the majority of cases of adrenal incidentalomas. This has created a dilemma for the proper diagnostic, clinical and radiologic follow-up as well as the triggers for surgical intervention. We present a series of three patients presenting with ACC that retrospectively arose from a small incidentally found adrenal lesion. Three patients were identified with ACC arising from an apparently benign adrenal incidentaloma. The average size of the original lesion was 1.6 cm whereas the average size of their adrenal tumor was 9.3 cm when they presented with ACC. Two of the three cases were found to develop functional tumors at the time of the diagnosis of ACC. Two of the three cases underwent surgical resection. The third patient was found to have metastatic disease at presentation and declined surgical intervention. We agree that current follow-up guidelines result in an increasing burden on our healthcare system; with expensive biochemical testing and imaging for what in most cases will prove to be a benign adenoma, these three cases have influenced our current strategies for follow-up. At the present time, we continue to follow the AAES/AACE guidelines. The development of improved methods of biochemical, radiologic and tissue diagnosis may help to improve our ability to recognize an ACC in this population at an earlier and potentially curable stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".