Adrenal Neoplasms: Lessons from Adrenal Multidisciplinary Tumor Boards
Bibliographic record
Abstract
The radiologic diagnosis of adrenal disease can be challenging in settings of atypical presentations, mimics of benign and malignant adrenal masses, and rare adrenal anomalies. Misdiagnosis may lead to suboptimal management and adverse outcomes. Adrenal adenoma is the most common benign adrenal tumor that arises from the cortex, whereas adrenocortical carcinoma (ACC) is a rare malignant tumor of the cortex. Adrenal cyst and myelolipoma are other benign adrenal lesions and are characterized by their fluid and fat content, respectively. Pheochromocytoma is a rare neuroendocrine tumor of the adrenal medulla. Metastases to the adrenal glands are the most common malignant adrenal tumors. While many of these masses have classic imaging appearances, considerable overlap exists between benign and malignant lesions and can pose a diagnostic challenge. Atypical adrenal adenomas include those that are lipid poor; contain macroscopic fat, hemorrhage, and/or iron; are heterogeneous and/or large; and demonstrate growth. Heterogeneous adrenal adenomas may mimic ACC, metastasis, or pheochromocytoma, particularly when they are 4 cm or larger, whereas smaller versions of ACC, metastasis, and pheochromocytoma and those with washout greater than 60% may mimic adenoma. Because of its nonenhanced CT attenuation of less than or equal to 10 HU, a lipid-rich adrenal adenoma may be mimicked by a benign adrenal cyst, or it may be mimicked by a tumor with central cystic and/or necrotic change such as ACC, pheochromocytoma, or metastasis. Rare adrenal tumors such as hemangioma, ganglioneuroma, and oncocytoma also may mimic adrenal adenoma, ACC, metastasis, and pheochromocytoma. The authors describe cases of adrenal neoplasms that they have encountered in clinical practice and presented to adrenal multidisciplinary tumor boards. Key lessons to aid in diagnosis and further guide appropriate management are provided. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".