PET/CT Imaging Characteristics of Gastric-Type Endocervical Adenocarcinoma: Findings from a Small Exploratory Series
Bibliographic record
Abstract
Objective: To identify distinctive 18F-FDG positron emission tomography (PET)/computer tomography (CT) features of gastric-type endocervical adenocarcinoma (GAS) that differentiate it from squamous cell carcinoma (SCC) and usual-type endocervical adenocarcinoma (UEA), as well as to correlate these findings with pathological characteristics. Methods: Patients treated between December 2018 and December 2024 were retrospectively reviewed. The study included 12 GAS, 48 SCC, and 30 UEA cases. Evaluated parameters included tumor morphology, cystic components, uterine cavity fluid, N/M staging, tumor diameter, the cervical lesion maximum standardized uptake value (SUVmax), and the tumor-to-liver maximum standardized uptake ratio (T/L SUVmax). Results: GAS predominantly exhibited diffuse infiltrative growth (11/12), in contrast to mass-like growth observed in SCC (37/48) and UEA (24/30) (both p < 0.001). Cystic components, uterine cavity fluid, and peritoneal metastasis occurred significantly more frequently in GAS (12/12, 11/12, 5/12, respectively) compared to SCC and UEA (all p < 0.001). Elevated CA19-9 levels were more common in GAS (9/12) compared with SCC (p < 0.001). Tumor diameter did not differ significantly among the groups (p > 0.05). SUVmax and T/L SUVmax values were significantly lower in GAS (7.5 ± 3.8 and 2.5 ± 1.6, respectively) than in UEA (19.1 ± 11.4 and 5.7 ± 3.4) and SCC (17.4 ± 6.7 and 5.5 ± 2.6) (all p < 0.001). Conclusion: The clinical characteristics of GAS include infiltrative tumor growth, fluid accumulation in the uterine cavity, frequent formation of microcystic or macrocystic components, peritoneal metastasis, and elevated CA19-9 levels. In this cohort, SUVmax and T/L SUVmax values in GAS were significantly lower than those observed in SCC and UEA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".