Comparison of computer simulations and clinical treatment results of magnetic resonance‐guided focused ultrasound surgery (MRgFUS) of uterine fibroids
Bibliographic record
Abstract
Abstract Purpose Magnetic resonance‐guided focused ultrasound surgery (MRgFUS) can be used to noninvasively treat symptomatic uterine fibroids by heating with focused ultrasound sonications while monitoring the temperature with magnetic resonance (MR) thermometry. While prior studies have compared focused ultrasound simulations to clinical results, studies involving uterine fibroids remain scarce. In our study, we perform such a comparison to assess the suitability of simulations for treatment planning. Methods Sonications (N = 67) were simulated retrospectively using acoustic and thermal models based on the Rayleigh integral and Pennes bioheat equation followed by MR‐thermometry simulation in seven patients who underwent MRgFUS treatment for uterine fibroids. The spatial accuracy of simulated focus location was assessed by evaluating displacements of the centers of mass of the thermal dose distributions between simulated and treatment MR thermometry slices. Temperature–time curves and sizes of 240 equivalent minutes at 43°C (240EM43) volumes between treatment and simulation were compared. Results The simulated focus location showed errors of 2.7 ± 4.1, −0.7 ± 2.0, and 1.3 ± 1.2 mm (mean ± SD) in the anterior–posterior, foot–head, and right–left directions for a fibroid absorption coefficient of 4.9 Np m–1 MHz–1and perfusion parameter of 1.89 kg m–3s–1. Linear regression of 240EM43volumes of 67 sonications of patient treatments and simulations utilizing these parameters yielded a slope of 1.04 and a correlation coefficient of 0.54. The temperature rise ratio of simulation to treatment near the end of sonication was 0.47 ± 0.22, 1.28 ± 0.60, and 1.49 ± 0.71 for 66 sonications simulated utilizing fibroid absorption coefficient of 1.2, 4.9, and 8.6 Np m–1 MHz–1, respectively, and the aforementioned perfusion value. The impact of perfusion on peak temperature rise is minimal between 1.89 and 10 kg m–3s–1, but became more substantial when utilizing a value of 100 kg m–3s–1. Conclusions The results of this study suggest that perfusion, while in some cases having a substantial impact on thermal dose volumes, has less impact than ultrasound absorption for predicting peak temperature elevation at least when using perfusion parameter values up to 10 kg m–3s–1for this particular array geometry, frequencies, and tissue target which is good for clinicians to be aware of. The results suggest that simulations show promise in treatment planning, particularly in terms of spatial accuracy. However, in order to use simulations to predict temperature rise due to a sonication, knowledge of the patient‐specific tissue parameters, in particular the absorption coefficient is important. Currently, spatially varying patient‐specific tissue parameter values are not available during treatment, so simulations can only be used for planning purposes to estimate sonication performance on average.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".