Severe Hyperandrogenism in A Premenopausal Woman With An Imaging-Negative Leydig Cell Tumor
Bibliographic record
Abstract
OBJECTIVE: Hirsutism and hyperandrogenism in premenopausal women are most often associated with polycystic ovarian syndrome. We present a case of progressive, severe hyperandrogenism with negative imaging identified on surgical histopathology as being due to a Leydig cell tumor (LCT), thus illustrating localization challenges associated with these small tumors. METHODS: Laboratory investigations included testosterone, dehydroepiandrosterone sulfate, 17-hydroxyprogesterone, luteinizing hormone, follicle-stimulating hormone, thyroid-stimulating hormone, 24-hour urine cortisol, and prolactin. Imaging included pelvic ultrasound, adrenal magnetic resonance imaging, and computed tomography. Ovarian vein sampling was not available. RESULTS: A 42-year-old woman presented with frontal alopecia, voice deepening, coarse facial hair, and amenorrhea on a background of lifelong oligomenorrhea. Peak testosterone was 30.2 nmol/L (female normal range is <2.0 nmol/L) with normal dehydroepiandrosterone sulfate, 17-hydroxyprogesterone, prolactin, 24-hour urine cortisol, and thyroid-stimulating hormone. Transvaginal ultrasound, adrenal magnetic resonance imaging, and computed tomography of the thorax and abdomen revealed no androgen source. Testosterone failed to suppress with gonadotropin-releasing hormone agonist. Although no abnormality was seen during oophorectomy, surgical pathology documented a 1.8-cm, well-circumscribed hilar LCT. Postoperative testosterone was <0.5 nmol/L. CONCLUSION: Although this patient had testosterone levels well into the masculine range, multiple imaging results were negative with a LCT found only after oophorectomy. LCTs are rare ovarian stromal tumors and while 50 to 70% of these tumors produce androgen, size and clinical severity may not be well correlated. This case report illustrates that despite an association with substantially elevated androgen levels, the small size of LCTs can result in localization challenges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".