Dataset Information

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

ABSTRACT:

Background

High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach.

Methods

A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 patients with uterine fibroids undergoing ultrasound-guided HIFU therapy (HIFU group). Here, preHIFU scans (n = 56), postHIFU imaging maximum one day after HIFU (n = 54), and the last available follow-up examination (n = 53, days after HIFU: 420 ± 377) were included. The training was performed on 80% of the data with fivefold cross-validation. The remaining data were used as a hold-out test set. Ground truth was generated by a board-certified radiologist and a radiology resident. For the assessment of inter-reader agreement, all preHIFU examinations were segmented independently by both.

Results

High segmentation performance was already observed for the default 3D nnU-Net (mean Dice score = 0.95 ± 0.05) on the validation sets. Since the CBAM nnU-Net showed no significant benefit, the less complex default model was applied to the hold-out test set, which resulted in accurate uterus segmentation (Dice scores: standard group 0.92 ± 0.07; HIFU group 0.96 ± 0.02), which was comparable to the agreement between the two readers.

Conclusions

This study presents a method for automatic uterus segmentation which allows a fast and consistent assessment of uterine volume. Therefore, this method could be used in the clinical setting for objective assessment of therapeutic response to HIFU therapy.

SUBMITTER: Theis M

PROVIDER: S-EPMC9813298 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

Theis Maike M Tonguc Tolga T Savchenko Oleksandr O Nowak Sebastian S Block Wolfgang W Recker Florian F Essler Markus M Mustea Alexander A Attenberger Ulrike U Marinova Milka M Sprinkart Alois M AM

Insights into imaging 20230105 1

<h4>Background</h4>High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach.<h4>Methods</h4>A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 p ...[more]

PMID: 36600120

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Project description:BackgroundPrediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning.Materials and methodsThis retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively.ResultsA total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit.ConclusionsThe radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

Project description:BackgroundMagnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is a noninvasive uterine-preserving treatment alternative to hysterectomy for women with symptomatic uterine leiomyomas (fibroids). Uterine fibroids commonly occur, have a broad impact on women's health and lifestyle, continue to be the main indication for hysterectomy, and represent a costly public health burden.ObjectivesThe objectives of the analysis were to evaluate patients' eligibility for MRgHIFU treatment of symptomatic uterine fibroids and the technical success, safety, effectiveness, and durability of this treatment. The review also compared the safety and effectiveness of MRgHIFU with other minimally invasive uterine-preserving treatments and surgeries for uterine fibroids.MethodsA literature search was performed on March 27, 2014, using Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid EMBASE, EBSCO Cumulative Index to Nursing & Allied Health Literature (CINAHL), and EBM Reviews, for studies published from January 1, 2000, to March 27, 2014.ResultsThe evidence review identified 2 systematic reviews, 2 RCTs, 45 cohort study reports, and 19 case reports involving HIFU treatment of symptomatic uterine fibroids. Eligibility for MRgHIFU treatment was variable, ranging from 14% to 74%. In clinical cohort studies involving 1,594 patients, 26 major complications (1.6%) were reported. MRgHIFU resulted in statistically and clinically significant reductions in fibroid-related symptoms in studies conducted in 10 countries, although few involved follow-up longer than 1 year. Retreatment rates following MRgHIFU were higher in early clinical studies involving regulated restrictions in the extent of fibroid ablation than in later reports involving near-complete ablation. Emergent interventions, however, were rare. Although a desire for fertility was an exclusion criteria for treatment, spontaneous term pregnancies did occur following HIFU. There were no randomized trials comparing MRgHIFU and other guidance methods, other minimally invasive treatments, or surgeries for symptomatic uterine fibroids. Limitations with MRgHIFU included restricted eligibility, requirement for a dedicated MR device to guide the treatment, lengthy procedure time, and loss of MR opportunity time.ConclusionsFor women failing medical therapy and seeking alternatives to hysterectomy for symptomatic uterine fibroids, MRgHIFU provides a safe and effective, noninvasive, uterine-preserving treatment from which they rapidly recover. The treatment advantages of MRgHIFU are potentially offset by restrictive eligibility, lengthy procedure time, and dependence on availability of an MR device. The lack of comparative evidence between MRgHIFU and other, more established uterine-preserving treatments limits informed decision making among treatment options.

Project description:Background: Uterine fibroids are common benign tumors among premenopausal women. High- intensity focused ultrasound (HIFU) is an emerging non-invasive intervention which uses the high-intensity ultrasound waves from ultrasound probes to focus on the targeted fibroids. However, the efficacy of HIFU in comparison with that of other common treatment types in clinical procedure remains unclear. Objective: To investigate the comparative effectiveness and safety of HIFU with other techniques which have been widely used in clinical settings. Methods: We searched the Cochrane Central Register of Controlled Trials, PubMed, EMBASE, Cumulative Index to Nursing & Allied Health Literature, Web of Science, ProQuest Nursing & Allied Health Database, and three Chinese academic databases, including randomized controlled trials (RCTs), non-RCTs, and cohort studies. The primary outcome was the rate of re-intervention, and the GRADE approach was used to interpret the findings. Results: About 18 studies met the inclusion criteria. HIFU was associated with an increased risk of re-intervention rate in comparison with myomectomy (MYO) [pooled odds ratio (OR): 4.05, 95% confidence interval (CI): 1.82-8.9]. The results favored HIFU in comparison with hysterectomy (HYS) on the change of follicle-stimulating hormone [pooled mean difference (MD): -7.95, 95% CI: -8.92-6.98), luteinizing hormone (MD: -4.38, 95% CI: -5.17-3.59), and estradiol (pooled MD: 43.82, 95% CI: 36.92-50.72)]. HIFU had a shorter duration of hospital stay in comparison with MYO (pooled MD: -4.70, 95% CI: -7.46-1.94, p < 0.01). It had a lower incidence of fever (pooled OR: 0.15, 95% CI: 0.06-0.39, p < 0.01) and a lower incidence of major adverse events (pooled OR: 0.04, 95% CI: 0.00-0.30, p < 0.01) in comparison with HYS. Conclusions: High-intensity focused ultrasound may help maintain feminity and shorten the duration of hospital stay. High-quality clinical studies with a large sample size, a long-term follow-up, and the newest HIFU treatment protocol for evaluating the re-intervention rate are suggested to be carried out. Clinical decision should be based on the specific situation of the patients and individual values.

Dataset Information

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

Background

Methods

Results

Conclusions

Publications

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

Similar Datasets

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