The Predictive Value of Extracellular Volume Fraction Based on T1 Mapping for the Efficacy of Microwave Ablation of Uterine Fibroids.

STUDY

OBJECTIVE

To evaluate the predictive value of native T1 and extracellular volume fraction (ECV) based on T1 mapping imaging for the postoperative efficacy of microwave ablation (MWA) of uterine fibroids.

DESIGN

prospective study.

SETTING

The First Hospital of Hebei Medical University.

PATIENTS: Fifty-nine patients with pathologically confirmed uterine fibroids who underwent microwave ablation (MWA) between January 2023 and January 2024.

INTERVENTIONS: microwave ablation.

MEASUREMENTS AND MAIN

RESULTS

All patients underwent preoperative conventional MRI, T1mapping and DWI imaging of the uterus. Conventional MRI features, native T1, ECV, and apparent diffusion coefficient (ADC) values were compared between the 2 groups. Prognostic risk factors were identified using univariate and multivariate logistic regression. ROC curves for postoperative outcomes were plotted based on risk factors. A total of 59 patients (134 uterine fibroids) were included, with 90 fibroids classified into the effective prognosis group and 44 into the ineffective prognosis group. Significant differences were observed between the two groups in terms of tumor signal homogeneity, presence of T2 hyperintensity, tumor border clarity, tumor volume, pre- and post-contrast T1 values, and ECV fraction. The areas under the ROC curve (AUC) for predicting treatment efficacy using pre- and post-contrast T1 values and ECV values of uterine fibroids were 0.867, 0.850, and 0.585, respectively. The AUCs for T1 mapping combined with ECV, conventional imaging features alone, and conventional imaging features plus T1 mapping parameters were 0.920, 0.869, and 0.953, respectively (P < 0.05).

CONCLUSION

The native T1 and ECV values had good predictive value for predicting the efficacy of MWA treatment for uterine fibroids, with the combination of conventional imaging features+T1 mapping parameters having the greatest predictive value.