Added Value of Contrast-enhanced US for Evaluation of Female Pelvic Disease
Bibliographic record
Abstract
Since the first application of contrast-enhanced US (CEUS) in the late 1960s, the use of US contrast agents has grown tremendously, and this examination has proved to be a valuable adjunct to diagnostic US for detection and characterization of disease. Also, CEUS has emerged as an excellent option for evaluation of indeterminate lesions that require additional imaging, given its excellent safety profile, including that in patients with end-stage renal disease or allergies to contrast material who are unable to undergo contrast-enhanced CT or MRI. US traditionally has been considered the imaging modality of choice for evaluation of the female pelvis, followed by MRI and rarely fluoroscopy, CT, PET, or angiography. CEUS has the potential to add significant value in imaging gynecologic disease, and indications for its use in the female pelvis are expected to continue evolving. It can aid in evaluation of nonvascular structures, such as assessment of tubal patency, uterine cavity morphology, and pelvic fistulas. CEUS can help characterize poorly vascularized gynecologic tumors or tissues with slow flow by using qualitative and quantitative parameters and aid in image-guided interventions or biopsies by facilitating visualization of lesions that are difficult to see with other imaging modalities. The authors provide an overview of current applications of US contrast agents in the female pelvis and discuss associated factors such as technique, interpretation, and image optimization. They also discuss the limitations of CEUS and describe its utility in the evaluation of female pelvic disease by using an organ system case-based approach. © RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".