Endoscopic ultrasound‐guided FNA of pelvic lesions: A large single‐center experience
Bibliographic record
Abstract
BACKGROUND: Pelvic endoscopic ultrasound-guided fine-needle aspiration (PEUS-FNA) of rectal or perirectal lesions is safe, minimally invasive, and well tolerated. It provides valuable information, which can greatly influence patient management. Herein, the authors present what to their knowledge is the largest series to date of PEUS-FNA. METHODS: PEUS-FNA specimens were retrieved from the archives of the study institution from January 2001 to March 2015. Only patients with solid pelvic lesions were examined. The cytopathology findings, immunohistochemistry, corresponding histology, and clinical data were collected. For analysis of accuracy, atypical or suspicious results were classified as "negative." The sensitivity and specificity of PEUS-FNA were calculated in a subset of patients with available surgical pathology. RESULTS: A total of 127 cases meeting the current study criteria were obtained from patients who underwent PEUS-FNA at the study institution between January 2001 and March 2015. The mean age of the patients was 60 years, and 53% were female. Pelvic lesions were comprised of 72% masses and 28% lymph nodes, with a mean mass diameter of 27.38 mm (range, 5-100 mm). PEUS-FNA was positive for malignancy in 45% of cases, atypical/suspicious in 4.7% of cases, and negative for malignancy in 50.3% of cases. Surgical pathology was available for 44 patients. PEUS-FNA demonstrated 89.3% sensitivity, 100% specificity, a diagnostic accuracy of 93.2%, a positive predictive value of 100%, and a negative predictive value of 84.2%. No complications were noted. CONCLUSIONS: PEUS-FNA is safe and effective for the investigation of pelvic lesions. Cancer Cytopathol 2016;124:836-41. © 2016 American Cancer Society.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".