Predicting the efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine leiomyomas based on DTI indicators and imaging features

Prediction of high-intensity focused ultrasound (HIFU)-induced lesion size using the echo amplitude from the focus in tissue

In the realm of high-intensity focused ultrasound (HIFU) therapy, the precise prediction of lesion size during treatment planning remains a challenge, primarily due to the difficulty in quantitatively assessing energy deposition at the target site and the acoustic properties of the tissue through which the ultrasound wave propagates. This study investigates the hypothesis that the echo amplitude originating from the focus is indicative of acoustic attenuation and is directly related to the resultant lesion size. Echoes from multi-layered tissues, specifically porcine tenderloin and bovine livers, with varying fat thickness from 0 mm to 35 mm were collected using a focused ultrasound (FUS) transducer operated at a low power output and short duration. Subsequent to HIFU treatment under clinical conditions, the resulting lesion areas in the ex vivo tissues were meticulously quantified. A novel treatment strategy that prioritizes treatment spots based on descending echo amplitudes was proposed and compared with the conventional raster scan approach. Our findings reveal a consistent trend of decreasing echo amplitudes and HIFU-induced lesion areas with the increasing fat thickness. For porcine tenderloin, the values decreased from 2541.7 ± 641.9 mV and 94.4 ± 17.9 mm2 to 385(342.5) mV and 24.9 ± 18.7 mm2, and for bovine liver, from 1406(1202.5) mV and 94.4 ± 17.9 mm2 to 502.1 ± 225.7 mV and 9.4 ± 6.3 mm2, respectively, as the fat thickness increases from 0 mm to 35 mm. Significant correlations were identified between preoperative echo amplitudes and the HIFU-induced lesion areas (R = 0.833 and 0.784 for the porcine tenderloin and bovine liver, respectively). These correlations underscore the potential for an accurate and dependable prediction of treatment outcomes. Employing the proposed treatment strategy, the ex vivo experiment yielded larger lesion areas in bovine liver at a penetration depth of 8 cm compared to the conventional approach (58.84 ± 17.16 mm2 vs. 44.28 ± 15.37 mm2, p < 0.05). The preoperative echo amplitude from the FUS transducer is shown to be a reflective measure of acoustic attenuation within the wave propagation window and is closely correlated with the induced lesion areas. The proposed treatment strategy demonstrated enhanced efficiency in ex vivo settings, affirming the feasibility and accuracy of predicting HIFU-induced lesion size based on echo amplitude.

Treatment of adenomyosis, abdominal wall endometriosis and uterine leiomyoma with interventional radiology: A review of current evidences

Interventional radiology shows promises in the field of women's health, particularly in pelvic interventions. This review article discusses the latest advancements in interventional radiology techniques for pelvic conditions affecting women including adenomyosis, abdominal wall endometriosis and uterine leiomyoma. Extraperitoneal endometriosis involving the abdominal wall may be treated by percutaneous thermal ablation, such as cryoablation, whereas uterine leiomyoma and adenomyosis can be managed either using percutaneous thermal ablation or using uterine artery embolization. Continued research and development in interventional radiology will further enhance the minimally-invasive interventions available for women's health, improving outcomes and quality of life for this large patient population of women.

Prediction of postoperative reintervention risk for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound ablation

OBJECTIVE

To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation.

METHODS

Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training (n = 125) or validation (n = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models.

RESULTS

Age (p < .001), fibroid volume (p = .001) and fibroid enhancement degree (p = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712-0.931) and 0.818 (95% CI: 0.694-0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, p < .001) and 29.5% (independent test cohort, p = .001) better than the clinical-imaging and radiomics models, respectively.

CONCLUSION

The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated.