Prediction using T2-weighted magnetic resonance imaging-based radiomics of residual uterine myoma regrowth after high-intensity focused ultrasound ablation

Ultrasound-based radiomics to predict the volume reduction rate of benign thyroid nodules after microwave ablation

OBJECTIVE

To evaluate the predictive power of ultrasound-based radiomics models for benign thyroid nodules with a volume reduction rate (VRR) of < or ≥75% at 12 months after microwave ablation.

METHODS

A retrospective study was conducted on 194 individuals with benign thyroid nodules who received ultrasound-guided microwave ablation between November 2019 and June 2023. The clinical and ultrasound features, including age, gender, volume, echogenicity, duration of ablation, and so on were analysed by t-test or chi-square test. Radiomics features were extracted from longitudinal and transverse ultrasound images of the nodules. The features were selected using methods such as least absolute shrinkage and selection operator (LASSO). Radiomics models were established using longitudinal, transverse, and longitudinal + transverse ultrasound images to predict the VRR of benign thyroid nodules after ablation. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curve analysis were used to assess the models' performance.

RESULTS

At 12 months following ablation, the VRR of the nodules was 77.8 ± 19.4% (7.4-98.8%). Statistical analysis revealed that the duration of ablation and the proportion of liquid extracted were significantly correlated with the 12-month VRR (P <0.05). In the radiomics models, Logistic Regression (LR) performed the best. In the training cohorts, the area under the curve (AUC) for the longitudinal, transverse, and combined groups were 0.935, 0.800, and 0.937. The AUC values in the test cohort were 0.820, 0.844, and 0.917.

CONCLUSION

The radiomics models established based on pre-ablation ultrasound images showed good predictive efficacy for the VRR of nodules at 12 months following ablation. The predictive efficacy is best in the combined group. With the models, we can preoperatively predict patients' prognoses and thereby determine whether to proceed with ablation therapy.

Progress in High Intensity Focused Ultrasound Ablation for Fertility Preservation Therapy of Uterine Fibroids and Adenomyosis

High intensity focused ultrasound (HIFU) is an effective and safe non-invasive treatment method, widely used in the treatment of uterine fibroids and adenomyosis in the field of gynecology. The side effects in HIFU is low in incidence and mild. HIFU can significantly alleviate the symptoms of patients, reduce lesion volumes, improve quality of life, and has good cost-effectiveness. HIFU can accurately ablate the uterine fibroids and adenomyosis lesions, without destroying normal myometrium and endometrium, and thus HIFU is a promising alternative to myomectomy in uterine fibroids patients with fertility desire. Several studies have shown that in terms of ovarian endocrine function protection, HIFU treatment is superior to uterine artery embolization, and similar to myomectomy. Existing limited researches show that patients with uterine fibroids have a favorable pregnancy rate and live birth rate, as well as a lower natural abortion rate after HIFU treatment. Pregnancy rate after HIFU treatment for uterine fibroids is not lower than myomectomy, and higher than uterine artery embolization. HIFU may have significant advantages in shortening pregnancy interval compared with myomectomy. However, the proportion of cesarean section delivery after HIFU treatment is relatively high, and gestational uterine rupture after HIFU treatment exist in literature. Higher quality clinical data is needed to confirm the pregnancy outcomes and safety after HIFU treatment in future.

The value of multi-parameter radiomics combined with imaging features in predicting the therapeutic efficacy of HIFU treatment for uterine fibroids

Objectives

To evaluate the effectiveness of high-intensity focused ultrasound (HIFU) therapy for treating uterine fibroids by utilizing multi-sequence magnetic resonance imaging radiomic models.

Methods

One hundred and fifty patients in our hospital were randomly divided into a training cohort (n=120) and an internal test cohort (n=30), and forty-five patients from another hospital serving as an external test cohort. Radiomics features of uterine fibroids were extracted and selected based on preoperative T2-weighted imaging fat suppression(T2WI-FS)and contrast-enhanced T1WI(CE-T1WI)images, and logistic regression was used to develop the T2WI-FS, CE-T1WI, and combined T2WI-FS + CE-T1WI models, along with the radiomics-clinical model integrating radiomics features with imaging characteristics. The performance and clinical applicability of each model were assessed through receiver operating characteristic (ROC) curve, decision curve analysis (DCA), as well as Network Readiness Index (NRI) and Integrated Discrimination Index (IDI).

Results

The AUC values of the radiomics-clinical model and the T2WI-FS + CE-T1WI model were the highest. In the training cohort, the radiomics-clinical model showed higher AUC values than the T2WI-FS + CE-T1WI model, while in the internal and external testing cohorts, the AUC values of the T2WI-FS + CE-T1WI model were higher than that of the radiomics-clinical model. DCA further demonstrated that these two models achieved the greatest net benefit. NRI and IDI analyses suggested that the T2WI-FS + CE-T1WI model had higher clinical utility.

Conclusions

Both the T2WI-FS + CE-T1WI model and the radiomics-clinical model demonstrate higher predictive value and larger net benefit compared to other models.

Predictive value of contrast-enhanced MRI for the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment

OBJECTIVES

To investigate whether the signal intensity (SI) ratio of residual fibroid (RF) to myometrium using Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) could predict fibroid regrowth after high-intensity focused ultrasound (HIFU) treatment.

MATERIALS AND METHODS

A retrospective analysis was conducted among 164 patients with uterine fibroids who underwent HIFU. To predict the RF regrowth, the SI perfusion parameters were quantified using the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU and then compared with the fibroid-myometrium SI ratio on the T2-weighted image (T2WI) and Funaki classification 1 year later. Thirty cases from another center were used as an external validation set to evaluate the performance of RF-myometrium SI ratio.

RESULTS

The predictive performance of the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU (Area Under Curve, AUC: 0.869) was superior to that of the preoperative and postoperative fibroid-myometrium SI ratios on the T2WI (AUC: 0.724, 0.696) and Funaki classification (AUC: 0.663, 0.623). Multivariate analysis showed that the RF- myometrium SI ratio and RF thickness were independent factors. The RF-myometrium SI ratio reflects the long-term rate of re-intervention (r = 0.455, p < 0.001).

CONCLUSION

The RF-myometrium SI ratio on CE-MRI exhibits greater accuracy in predicting RF regrowth compared to the SI classification and the SI ratio on T2WI.

CRITICAL RELEVANCE STATEMENT

The ratio of residual uterine fibroid to myometrial signal intensity on contrast-enhanced (CE)-MRI can reflect residual blood supply, predict regrowth of fibroids, and thus reflect long-term re-intervention rate and recovery situation of clinical high-intensity focused ultrasound (HIFU) treatment.

KEY POINTS

Contrast-enhanced (CE)-MRI can indicate the blood supply of residual uterine fibroids after high-intensity focused ultrasound (HIFU) treatment. The predictive capability of CE-MRI ratio surpasses T2WI ratio and the Funaki. Residual fibroids can serve as a measure of the long-term efficacy of HIFU.

Robust Radiomics Models for Predicting HIFU Prognosis in Uterine Fibroids Using SHAP Explanations: A Multicenter Cohort Study

This study sought to develop and validate different machine learning (ML) models that leverage non-contrast MRI radiomics to predict the degree of nonperfusion volume ratio (NVPR) of high-intensity focused ultrasound (HIFU) treatment for uterine fibroids, equipping clinicians with an early prediction tool for decision-making. This study conducted a retrospective analysis on 221 patients with uterine fibroids who received HIFU treatment and were divided into a training set (N = 117), internal validation (N = 49), and an external test set (N = 55). The 851 radiomics features were extracted from T2-weighted imaging (T2WI), and the max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. Several ML models were constructed by logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM). These models underwent internal and external validation, and the best model's feature significance was assessed via the Shapley additive explanations (SHAP) method. Four significant non-contrast MRI radiomics features were identified, with the SVM model outperforming others in both internal and external validations, and the AUCs of the T2WI models were 0.860, 0.847, and 0.777, respectively. SHAP analysis highlighted five critical predictors of postoperative NVPR degree, encompassing two radiomics features from non-contrast MRI and three clinical data indicators. The SVM model combining radiomics features and clinical parameters effectively predicts NVPR degree post-HIFU, which enables timely and effective interventions of HIFU.

Risk Factors and Prediction Nomogram of Local Regeneration After Ultrasound-Guided Microwave Ablation of Uterine Fibroids

STUDY OBJECTIVE

To explore the risk factors associated with local regeneration of the treated uterine fibroids (UFs) after microwave ablation (MWA) and to develop a nomogram model for predicting the risk of local regeneration.

DESIGN

Retrospective study.

SETTING

The Eighth Affiliated Hospital of Sun Yat-Sen University.

PATIENTS

Patients with UFs who underwent MWA at our hospital between October 2020 and April 2023 were included.

INTERVENTION

MWA was used for the treatment of UFs.

MEASUREMENTS AND MAIN RESULTS

A total of 47 patients with 68 fibroids were included into this study. Over a median follow-up of 13 months (interquartile range, 8-22 months), local regeneration occurred in 11 UFs. The clinical and imaging characteristics of these patients were recorded and compared. Risk factors for local regeneration were determined through univariate and multivariate Cox regression analysis. Multivariate analysis revealed that the fertility desires, larger size of UFs (≥95.3 cm3), and hyperenhancement of UFs on contrast-enhanced ultrasound were independent risk factors for local regeneration after MWA. A predictive nomogram was constructed to predict the local regeneration after MWA of UFs. The concordance index (C-index) (C-index, 0.924; internal validation C-index, 0.895) and the 1- and 2-year area under the curve values (0.962, 0.927) all indicated that the nomogram had good predictive performance. Calibration and decision curve analysis curves further confirmed the model's accuracy and clinical utility.

CONCLUSION

Fertility desires, larger size of UFs, and hyperenhancement on contrast-enhanced ultrasound were independent predictors of UFs local regeneration after MWA in our study. The nomogram constructed based on the abovementioned independent risk factors may help predict which UFs will develop local regeneration after MWA.

Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus

Objective

To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus.

Materials and methods

In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model.

Results

Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models.

Conclusion

Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.

MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis

Background: MRI diagnostics are important for adenomyosis, especially in cases with inconclusive ultrasound. This study assessed the potential of MRI-based radiomics as a novel tool for differentiating between uteri with and without adenomyosis. Methods: This retrospective proof-of-principle single-center study included nine patients with and six patients without adenomyosis. All patients had preoperative T2w MR images and histological findings served as the reference standard. The uterus of each patient was segmented in 3D using dedicated software, and 884 radiomics features were extracted. After dimension reduction and feature selection, the diagnostic yield of individual and combined features implemented in the machine learning models were assessed by means of receiver operating characteristics analyses. Results: Eleven relevant radiomics features were identified. The diagnostic performance of individual features in differentiating adenomyosis from the control group was high, with areas under the curve (AUCs) ranging from 0.78 to 0.98. The performance of ML models incorporating several features was excellent, with AUC scores of 1 and an area under the precision-recall curve of 0.4. Conclusions: The set of radiomics features derived from routine T2w MRI enabled accurate differentiation of uteri with adenomyosis. Radiomics could enhance diagnosis and furthermore serve as an imaging biomarker to aid in personalizing therapies and monitoring treatment responses.

Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids

Background

The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it is difficult for an accurate prediction of the efficacy of high intensity focused ultrasound (HIFU) ablation in fibroids before the treatment. The purpose of this study was to set up a radiomics model based on MRI T2-weighted imaging (T2WI) for predicting the efficacy of HIFU ablation in uterine fibroids, and it would be used in preoperative screening of the fibroids for achieving high non-perfused volume ratio (NPVR).

Methods

A total of 178 patients with uterine fibroids were consecutively enrolled and treated with ultrasound-guided HIFU under conscious sedation between February 2017 and December 2021. Among them, 96 patients with 108 uterine fibroids with high ablation efficacy (NPVR ≥80%, h_NPVR) and 82 patients with 92 fibroids with lower ablation efficacy (NPVR <80%, l_NPVR) were retrospectively analyzed. The transverse T2WI images of fibroids were selected, and the fibroids were delineated slice by slice using ITK-SNAP software. The radiomics analysis was performed to find the imaging biomarker for the construction of a predicting model for the evaluation of the ablation efficacy, including the feature extraction, feature selection and model construction. The prediction model was built by logistic regression and assessed by receiver operating characteristic (ROC) curve, and the prediction efficiency of the two models was compared by Delong test. The ratio of the training set to the testing set was 8:2.

Results

The logistic regression model showed that the mean area under the curve (AUC) of the training set was 0.817 [95% confidence interval (CI): 0.755-0.882], and the testing set was 0.805 (95% CI: 0.670-0.941), respectively, which indicated a strong classification ability. The Delong test showed that there was no significant difference in the area under the ROC curve between the training set and testing set (P>0.05).

Conclusions

The radiomics model based on T2WI is feasible and effective for predicting the efficacy of HIFU ablation in treatment of uterine fibroids.

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters

BACKGROUND

Prediction 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 METHODS

This 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.

RESULTS

A 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.

CONCLUSIONS

The 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.

Successful pregnancies in women with diffuse uterine leiomyomatosis after high-intensity focused ultrasound ablation: report of three cases

OBJECTIVE

To describe the reproductive outcomes of patients with diffuse uterine leiomyomatosis (DUL) treated with high-intensity focused ultrasound (HIFU) ablation.

MATERIALS AND METHODS

Three patients of reproductive age with DUL who underwent HIFU treatment were enrolled, all of whom had a strong desire to become pregnant. All patients underwent routine laboratory tests, electrocardiography (ECG), chest X-ray radiography, ultrasound, and magnetic resonance imaging (MRI) examinations after routine medical history collection and physical examination. The treatment time, treatment power, sonication time, and adverse events were recorded. One day after HIFU, MRI was performed to evaluate treatment efficacy. The patients were scheduled for follow-up at 3-, 6-, 12-, and 24-month after HIFU treatment.

RESULTS

All the three patients completed HIFU treatment successfully without any major complication. Uterine size and menstrual volume significantly decreased with the combination of medical and HIFU treatments. The shrinkage rate of uterine volume was 31-44% and the menstrual volume reduced by 1/2 or returned to normal at 3 months post-HIFU. Three patients had successful conceptions between 3 and 11 months after HIFU with healthy deliveries. No uterine rupture occurred during pregnancy and delivery.

CONCLUSION

HIFU ablation may help achieve a successful pregnancy in patients with DUL.

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.