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Transcervical microwave ablation in type 2 uterine fibroids via a hysteroscopic approach: analysis of ablation profiles.

Ghina ZiaJan SebekJessica SchenckPunit Prakash
Published in: Biomedical physics & engineering express (2021)
Type 2 uterine fibroids are challenging to resect surgically as ≥ 50% volume of myoma lies within the myometrium. A hysteroscopic approach for ablating fibroids is minimally-invasive, but places a considerable burden on the operator to accurately place the ablation applicator within the target. We investigated the sensitivity of transcervical microwave ablation outcome with respect to position of the ablation applicator within 1 - 3 cm type 2 fibroids.Methods:A finite element computer model was developed to simulate 5.8 GHz microwave ablation of fibroids and validated with experiments inex vivotissue. The ablation outcome was evaluated with respect to applicator insertion angles (30°, 45°, 60°) , depth and offset from the fibroid center (±2 mm for 3 cm fibroid and ±1 mm for 1 cm fibroid) with 35 W and 15 W applied power for 3 cm and 1 cm fibroids, respectively. Power deposition was stopped when thermal dose of 40 cumulative equivalent minutes at 43 °C (CEM43) was accrued in adjacent myometrium.Results:Within the range of all evaluated insertion angles, depths and offsets, the ablation coverage was less sensitive to variation in angle as compared to depth and offset, and ranged from 34.9 - 83.6% for 3 cm fibroid in 140 - 400 s and 34.1 - 67.9% for 1 cm fibroid in 30 - 50 s of heating duration. Maximum achievable ablation coverage in both fibroid cases reach ∼ 90% if thermal dose is allowed to exceed 40 CEM43 in myometrium.Conclusion:The study demonstrates the technical feasibility of transcervical microwave ablation for fibroid treatment and the relationship between applicator position within the fibroid and fraction of fibroid that can be ablated while limiting thermal dose in adjacent myometrium.
Keyphrases
  • radiofrequency ablation
  • catheter ablation
  • atrial fibrillation
  • healthcare
  • machine learning
  • deep learning
  • pregnancy outcomes
  • optical coherence tomography
  • pregnant women