Necrosis in Preoperative Cross-Sectional Imaging and Postoperative Histology Is a Diagnostic Marker for Malignancy of Adrenocortical Tumors
Bibliographic record
Abstract
Necrosis in postoperative histology has been reported as being specific for adrenocortical carcinoma (ACC) compared to adenoma. We therefore retrospectively analyzed the diagnostic accuracy of the finding of necrosis in preoperative cross-sectional imaging and postoperative histology as a marker for ACC in our patient cohort. Among the 411 adrenalectomies in 396 patients performed between 2008 and April 2022, 30 cases of ACC (7.6%) were identified, with one tumor measuring less than 40 mm excluded. All 45 benign adrenocortical tumors of at least 40 mm in diameter, including Cushing, Conn, and hormonally inactive adenomas, served as controls. Preoperative imaging was available for 40 benign and 27 malignant adrenocortical tumors. In total, 10 of 40 (25%) benign adrenocortical tumors and 22 of 27 (81%) ACCs showed signs of possible necrosis in preoperative imaging. Pathologic examination confirmed necrosis in 1 of 40 (2.5%) benign tumors and in 26 out of 27 (96%) malignant tumors. The specificities of possible necrosis in preoperative imaging and necrosis in histology for diagnosing ACC were 75% and 97.5%, respectively, whereas the sensitivities were 81% and 96%, respectively. Signs of possible necrosis in radiologic imaging and tumor necrosis in histology proved to be very good predictive markers for the diagnosis of malignant adrenocortical tumors.
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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 | 0.001 |
| 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".