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Articles

Feasibility of apparent diffusion coefficient in predicting the technical outcome of MR-guided high-intensity focused ultrasound treatment of uterine fibroids – a comparison with the Funaki classification

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Pages 85-94 | Received 16 Mar 2020, Accepted 06 Jan 2021, Published online: 28 Jan 2021
 

Abstract

Purpose

To investigate the feasibility of using an apparent diffusion coefficient (ADC) classification in predicting the technical outcome of magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) treatment of symptomatic uterine fibroids and to compare it to the Funaki classification.

Materials and methods

Forty-two patients with forty-eight uterine fibroids underwent diffusion-weighted imaging (DWI) before MRgHIFU treatment. The DW images were acquired with five different b-values. Correlations between ADC values and treatment parameters were assessed. Optimal ADC cutoff values were determined to predict technical outcomes, that is, nonperfused volume ratios (NPVr) such that three classification groups were created (NPVr of <30%, 30–80%, or >80%). Results were compared to the Funaki classification using receiver-operating-characteristic (ROC) curve analysis, with statistical significance being tested with the Chi-square test.

Results

A statistically significant negative correlation (Spearman’s ρ = –0.31, p-value < 0.05) was detected between ADC values and NPV ratios. ROC curve analysis indicated that optimal ADC cutoff values of 980 × 10−6mm2/s (NPVr > 80%) and 1800 × 10−6mm2/s (NPVr < 30%) made it possible to classify fibroids into three groups: ADC I (NPVr > 80%), ADC II (NPVr 30–80%) and ADC III (NPVr < 30%). Analysis of the whole model area under the curve resulted in values of 0.79 for the ADC classification (p-value = 0.0007) and 0.62 for the Funaki classification (p-value = 0.0527).

Conclusions

Lower ADC values prior to treatment correlate with higher NPV ratios. The ADC classification seems to be able to predict the NPV ratio and may even outperform the Funaki classification. Based on these results DWI and ADC maps should be included in the MRI screening protocol.

Acknowledgment

The authors acknowledge Saija Hurme, the biostatistician of Turku University, who kindly provided statistical advice in the preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study has received funding from The Finnish Cultural Foundation, TYKS Foundation, and Instrumentarium Science Foundation.