Evaluation of the Reproducibility of Bolus Transit Quantification With Contrast-Enhanced Ultrasound Across Multiple Scanners and Analysis Software Packages—A Quantitative Imaging Biomarker Alliance Study
Bibliographic record
Abstract
OBJECTIVES: Contrast enhanced ultrasound (CEUS) is now broadly used clinically for liver lesion detection and characterization. Obstacles to the efforts to quantify perfusion with CEUS have been the lack of a standardized approach and undocumented reproducibility. The use of multiple scanners and different analysis software packages compounds the degree of variability. Our objectives were to standardize a CEUS-based approach for quantification of perfusion-related parameters of liver lesions and to evaluate the variability of bolus transit parameters (rise time [RT], mean transit time [MTT], peak intensity, and area under the curve) obtained from various clinical ultrasound scanners and analysis software. MATERIALS AND METHODS: Bolus transit as a way of evaluating perfusion has been investigated both in vivo and in vitro in the past but without establishing its reproducibility. We developed a tissue flow phantom that produces time-intensity curves very similar to those extracted from clinical cine loops of liver lesions. We evaluated the variability of the bolus transit parameters with 4 commercial scanners (Philips iU22, Philips EPIQ, GE LOGIQ E9, and Siemens Acuson Sequoia) and 3 different analysis software packages in multiple trials (15 per scanner). RESULTS: The variability (coefficient of variation) from repeated trials and while using a single scanner and software was less than 8% for RT, less than 12% for MTT, less than 49% for peak intensity, and less than 50% for area under the curve. Currently, it is not possible to directly compare amplitude values from different scanners and analysis software packages owing to the arbitrary linearization algorithm used among manufacturers; however, it is possible for time parameters (RT and MTT). The variability when using a different scanner with the same analysis software package was less than 9% for RT and less than 21% for MTT. The variability when using a different analysis software with the same scanner was less than 9% for RT and less than 15% for MTT. In all the evaluations we have performed, RT is the least variable parameter, while MTT is only slightly more variable. CONCLUSIONS: The present study will lay the groundwork for multicenter patient evaluations with CEUS quantification of perfusion-related parameters with the bolus transit technique. When using the protocol and method developed here, it is possible to perform perfusion quantification on different scanners and analysis software and be able to compare the results. The current work is the first study that presents a comparison of bolus transit parameters derived from multiple systems and software packages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".