Secondary Involvement of the Uterine Cervix by Nongynecologic Neoplasms
Bibliographic record
Abstract
Secondary involvement of the uterine cervix by nongynecologic neoplasms is rare accounting for <2% of metastases to the gynecologic tract. This study aimed to analyze the clinicopathologic features of cervical involvement by nongynecologic malignancies. A total of 47 cases were identified including 39 (83%) carcinomas, 6 lymphomas (12.8%), and 2 (4.2%) cutaneous malignant melanomas. The most common primary site of origin among carcinomas was the gastrointestinal tract (27, 69.2%), followed by breast and urothelium (5 each, 12.8%), gallbladder, and lung (1 each, 2.6%). The gynecologic tract was involved at the presentation in 16 patients (34%), including 5 (10.6%) with the cervix being the first site, 7 (14.9%) with synchronous involvement of the cervix and other gynecologic sites, and 4 (8.5%) with the involvement of other gynecologic sites before the cervical presentation. Patients with lymphoma were younger compared with those with carcinoma (43.7 vs. >50.5) (P=0.01). Mean time to identification of cervical metastases was <1 year for gallbladder carcinoma, melanomas, and gastrointestinal signet ring cell carcinomas (P=0.03). Features that varied with different types of metastatic tumor included lymphovascular space invasion, depth of stromal invasion, growth pattern (glands lacking architectural complexity, cribriforming, solid), presence of goblet cells, and signet ring cells, degree of cytologic atypia, and overall findings mimicking a benign/noninvasive process (P≤0.027). Six tumors (12.8%) were initially misdiagnosed as cervical primary. Metastatic nongynecologic tumors can mimic primary in situ or invasive neoplasms in both ectocervix and endocervix. In patients with a known prior malignancy, the clinical history with ancillary studies and a high level of suspicion are crucial to ensure accurate diagnosis.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".