Inez M Verpalen1, Kimberley J Anneveldt2,3, Pieter C Vos4, Mireille A Edens5, Edwin Heijman4,6, Ingrid M Nijholt2, Jeroen R Dijkstra3, Joke M Schutte3, Arie Franx7, Lambertus W Bartels8, Chrit T W Moonen8, Martijn F Boomsma2. 1. Department of Radiology, Isala Hospital, Dokter van Heesweg 2, 8025, Zwolle, The Netherlands. i.m.verpalen@isala.nl. 2. Department of Radiology, Isala Hospital, Dokter van Heesweg 2, 8025, Zwolle, The Netherlands. 3. Department of Gynecology, Isala, Zwolle, The Netherlands. 4. Oncology Solutions, Philips Research, Eindhoven, The Netherlands. 5. Department of Innovation and Science, Isala, Zwolle, The Netherlands. 6. Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany. 7. Department of Obstetrics and Gynaecology, Erasmus Medical Center, Rotterdam, the Netherlands. 8. Imaging Division, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Abstract
BACKGROUND: Although the biological characteristics of uterine fibroids (UF) have implications for therapy choice and effectiveness, there is limited MRI data about these characteristics. Currently, the Funaki classification and Scaled Signal Intensity (SSI) are used to predict treatment outcome but both screening-tools appear to be suboptimal. Therefore, multiparametric and quantitative MRI was studied to evaluate various biological characteristics of UF. METHODS: 87 patients with UF underwent an MRI-examination. Differences between UF tissues and myometrium were investigated using T2-mapping, Apparent Diffusion Coefficient (ADC) maps with different b-value combinations, contrast-enhanced T1-weighted and T2-weighted imaging. Additionally, the Funaki classification and SSI were calculated. RESULTS: Significant differences between myometrium and UF tissue in T2-mapping (p = 0.001), long-TE ADC low b-values (p = 0.002), ADC all b-values (p < 0.001) and high b-values (p < 0.001) were found. Significant differences between Funaki type 3 versus type 1 and 2 were observed in SSI (p < 0.001) and T2-values (p < 0.001). Significant correlations were found between SSI and T2-mapping (p < 0.001; ρs = 0.82), ADC all b-values (p = 0.004; ρs = 0.31), ADC high b-values (p < 0.001; ρs = 0.44) and long-TE ADC low b-values (p = 0.004; ρs = 0.31). CONCLUSIONS: Quantitative MR-data allowed us to distinguish UF tissue from myometrium and to discriminate different UF tissue types and may, therefore, be a useful tool to predict treatment outcome/determine optimal treatment modality.
BACKGROUND: Although the biological characteristics of uterine fibroids (UF) have implications for therapy choice and effectiveness, there is limited MRI data about these characteristics. Currently, the Funaki classification and Scaled Signal Intensity (SSI) are used to predict treatment outcome but both screening-tools appear to be suboptimal. Therefore, multiparametric and quantitative MRI was studied to evaluate various biological characteristics of UF. METHODS: 87 patients with UF underwent an MRI-examination. Differences between UF tissues and myometrium were investigated using T2-mapping, Apparent Diffusion Coefficient (ADC) maps with different b-value combinations, contrast-enhanced T1-weighted and T2-weighted imaging. Additionally, the Funaki classification and SSI were calculated. RESULTS: Significant differences between myometrium and UF tissue in T2-mapping (p = 0.001), long-TE ADC low b-values (p = 0.002), ADC all b-values (p < 0.001) and high b-values (p < 0.001) were found. Significant differences between Funaki type 3 versus type 1 and 2 were observed in SSI (p < 0.001) and T2-values (p < 0.001). Significant correlations were found between SSI and T2-mapping (p < 0.001; ρs = 0.82), ADC all b-values (p = 0.004; ρs = 0.31), ADC high b-values (p < 0.001; ρs = 0.44) and long-TE ADC low b-values (p = 0.004; ρs = 0.31). CONCLUSIONS: Quantitative MR-data allowed us to distinguish UF tissue from myometrium and to discriminate different UF tissue types and may, therefore, be a useful tool to predict treatment outcome/determine optimal treatment modality.
Authors: K J Anneveldt; I M Verpalen; I M Nijholt; J R Dijkstra; R D van den Hoed; M Van't Veer-Ten Kate; E de Boer; J A C van Osch; E Heijman; H R Naber; E Ista; A Franx; S Veersema; J A F Huirne; J M Schutte; M F Boomsma Journal: Insights Imaging Date: 2021-12-18
Authors: Kimberley J Anneveldt; Heleen J van 't Oever; Inez M Verpalen; Ingrid M Nijholt; Wilbert Bartels; Jeroen R Dijkstra; Rolf D van den Hoed; Miranda van 't Veer-Ten Kate; Erwin de Boer; Sebastiaan Veersema; Judith A F Huirne; Joke M Schutte; Martijn F Boomsma Journal: Eur J Radiol Open Date: 2022-03-21