期刊
RADIOLOGY
卷 249, 期 1, 页码 187-194出版社
RADIOLOGICAL SOC NORTH AMERICA
DOI: 10.1148/radiol.2491071600
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资金
- InSightec (Haifa, Israel)
- National Institutes of Health [U41RR019703, P01CA067165]
- Hungarian State Eotvos Scholarship
Purpose: To retrospectively assess the magnetic resonance (MR) imaging predictors of success at reducing uterine leiomyoma volume and achieving patient symptom relief 12 months after MR imaging-guided focused ultrasound surgery. Materials and Methods: This single-center retrospective analysis of 71 symptomatic fibroids in 66 women was approved by the institutional review board and was HIPAA-compliant. Patients were treated with MR imaging-guided focused ultrasound surgery. The volume of treated fibroid and nonperfused volume (NPV) were calculated with software, while symptom outcome was assessed with a symptom severity score (SSS). Fibroids were classified as hyperintense or hypointense relative to skeletal muscle on pretreatment T2-weighted MR images. Results: Baseline volume of treated fibroids was 255.5 cm(3) +/- 201.7 (standard deviation), and baseline SSS was 61.5 +/- 14.9. Both pretreatment fibroid signal intensity (SI) and post-treatment NPV predicted 12-month volume reduction independently: Fibroids with an NPV of at least 20% or with low SI both showed significantly larger volume reduction (17.0% +/- 13.0 and 17.2% +/- 20.1, respectively) than fibroids with an NPV less than 20% or with high SI (10.7% +/- 18.2 and no significant change, respectively). Patients whose fibroids demonstrated an NPV of at least 20% also experienced a larger decrease in SSS than did patients with fibroids with an NPV less than 20% (50.1% +/- 19.8 vs 32.6% +/- 29.9). Conclusion: Fibroids with low SI on pretreatment T2-weighted MR images were more likely to shrink than were ones with high SI. The larger the NPV immediately after treatment, the greater the volume reduction and symptom relief were. These findings may help both in selecting appropriate patients for MR-guided focused ultrasound surgery and in predicting patient outcome. (C) RSNA, 2008.
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