Contrast‐Enhanced Ultrasound of the Ovary Technique and Lexicon Recommendations
Bibliographic record
Abstract
Diagnosis of ovarian malignancy in radiology is challenging, as there is significant overlap in imaging appearances along the spectrum of benign to malignant disease. In 2021, the American College of Radiology introduced the Ovarian-Adnexal Reporting and Data System (O-RADS) to standardize lesion description and improve consistency of interpretation and management of suspicious masses based on standard greyscale and Doppler ultrasound. Although endovaginal ultrasound (EVS) is well-established as a first-line investigation for ovarian lesions, it previously lacked the ability to show blood flow at the capillary level, severely limiting its contribution to patient care. The introduction of microbubble contrast agents and the subsequent development of contrast-enhanced ultrasound (CEUS) software techniques for endovaginal probes have allowed ultrasound to characterize perfusion-level vascularity of ovarian masses comparable to magnetic resonance (MR) and computed tomography (CT) scans. Today, there is limited North American literature addressing the utilization of CEUS in ovarian cancer. Given the advantages of EVS and CEUS, we propose a lexicon to standardize the description of qualitative and quantitative CEUS parameters with respect to ovarian masses. We emphasize the need for future development of specific CEUS criteria, including quantitative thresholds to aid in the differentiation of benign and malignant blood flow criteria. Our recommendation includes a safe, non-invasive, readily available technique, which provides high accuracy for diagnosis.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".