MRI of the endometrium - from normal appearances to rare pathology

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.

AIM

To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.

METHODS

The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.

RESULTS

Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.

CONCLUSION

The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Intrauterine chilled saline instillation reduces endometrial impairment on MRI after ultrasound-guided percutaneous microwave ablation of uterine adenomyosis

OBJECTIVE

To investigate whether intrauterine chilled saline can reduce endometrial impairment during US-guided percutaneous microwave ablation (PMWA) of adenomyosis.

METHODS

An open-label, randomized trial was conducted with sixty symptomatic adenomyosis patients who were randomly assigned (1:1) to receive PMWA treatment assisted by intrauterine saline instillation (study group) or traditional PMWA treatment alone (control group). The primary endpoint was endometrial perfusion impairment grade on post-ablation contrast-enhanced MRI. The secondary endpoints were endometrial dehydration grade, ablation rate, and intra-ablation discomfort.

RESULTS

The baseline characteristics of the two groups were similar. The incidence rates of endometrial perfusion impairment on MRI in the study and control groups were 6.7% (2/30) and 46.7% (14/30), respectively (p < 0.001). There were 28 (93.3%), 2 (6.7%), 0, and 0 patients in the study group and 16 (53.3%), 7 (23.3%), 5 (16.7%), and 2 (6.7%) in the control group (p < 0.001) who had grade 0, 1, 2, and 3 perfusion impairment, respectively. Additionally, there were 27 (90%), 3 (10%), and 0 patients in the study group and 19 (63.3%), 10 (33.3%), and 1 (3.3%) in the control group who had grade 0, 1, and 2 endometrial dehydration (p = 0.01). The ablation rates achieved in the study and control groups were 93.3 ± 17% (range: 69.2-139.6%) and 99.7 ± 15.7% (range: 71.5-129.8%), and they were not significantly different (p = 0.14). No significant difference was found in the intra-ablation discomfort.

CONCLUSION

Intrauterine chilled saline can effectively reduce endometrial impairment after PMWA treatment for adenomyosis.

CRITICAL RELEVANCE STATEMENT

This trial demonstrated that the instillation of intrauterine chilled saline reduced endometrial impairment on MRI during PMWA of adenomyosis. This approach allows more precise and safe ablation in clinical practice.

KEY POINTS

Endometrial impairment occurs in the PMWA treatment of adenomyosis. Intrauterine chilled saline can reduce endometrial impairment during PMWA for adenomyosis. An intrauterine catheter is a practical endometrial protecting method during thermal ablation.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2100053582. Registered 24 November 2021, www.chictr.org.cn/showproj.html?proj=141090 .

Abnormal Uterine Bleeding: A Pictorial Review on Differential Diagnosis and Not-So-Common Cases of Interventional Radiology Management

Abnormal uterine bleeding (AUB) poses a multifaceted challenge in women's health, necessitating an integrated approach that addresses its diverse etiologies and clinical presentations. The International Federation of Gynecology and Obstetrics PALM-COEIN classification system provides a systematic approach to the diagnosis of AUB in non-pregnant women, based on clinical and imaging-based categorization of causes into structural (Polyps, Adenomyosis, Leiomyomas and Malignancy; PALM), and non-structural causes (Coagulopathies, Ovulatory disorders, primary Endometrial disorders, Iatrogenic and Not otherwise classified; COEIN). On the other hand, placental disorders, uterine rupture, ectopic pregnancy and retained products of conceptions are the main causes of uterine bleeding during pregnancy and in the peripartum period. Ultrasound is usually the first-line imaging technique for the differential diagnosis of causes of AUB. Computed Tomography may be useful if ultrasound findings are unclear, especially in emergency settings. Magnetic resonance imaging, when indicated, is an excellent second-line diagnostic tool for a better non-invasive characterization of the underlying cause of AUB. This pictorial review aims to illustrate the main causes of AUB from the point of view of diagnostic imaging and to show not-so-common cases that can be treated by means of interventional radiology.

Role of T2 mapping of magnetic resonance imaging in the differentiation of endometrial cancer and benign endometrial lesions

PURPOSE

The T2 mapping of magnetic resonance imaging (MRI) in endometrial cancer (EC), benign endometrial lesions (BELs), and normal endometrium (NE) has rarely been reported. This study aimed to determine the T2 values of MRI in EC, BELs, and NE to investigate whether the T2 values can differentiate them and to assess the aggressiveness of EC.

METHODS

In total, 73 patients [EC, 51 (age, 57.4 ± 5.4 years); BELs, 22 (age, 57.8 ± 11.8 years)] and 23 normal volunteers (age, 56.1 ± 6.6 years) were included. The T2 values of MRI of the EC (type I and II), BEL, and NE groups were described and compared. The relationships between the T2 values of MRI in EC and the pathological characteristics [International Federation of Gynecology and Obstetrics (FIGO) stage and grade] were analyzed.

RESULTS

The median T2 values of NE, BEL, and EC were 197.5 (142.9-324.0) ms, 131.1 (103.2-247.9) ms, and 103.0 (71.6-243.5) ms (P < 0.001), respectively. The median T2 values of type I and type II EC were 100.8 (71.62-130.44) ms and 125.7 (119.7-243.5) ms, respectively. There were significant differences in the T2 values among the NE, BEL, type I EC, and type II EC groups (P < 0.001) except for between the type II EC and BEL groups (P = 0.938). The T2 value of MRI in type I EC was significantly lower than that in type II EC (P = 0.001). There were no significant differences in patients with type I EC having different FIGO stages (P = 0.273) or tumor grades (P = 0.686).

CONCLUSION

T2 mapping of MRI has the potential to quantitatively differentiate between EC, BELs, and NE as well as between type I and type II EC.

MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer

High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by "ProMisE". This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management.

Update on MRI in Evaluation and Treatment of Endometrial Cancer

Endometrial cancer is the second most common gynecologic cancer worldwide and the most common gynecologic cancer in the United States, with an increasing incidence in high-income countries. Although the International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer is a surgical staging system, contemporary published evidence-based data and expert opinions recommend MRI for treatment planning as it provides critical diagnostic information on tumor size and depth, extent of myometrial and cervical invasion, extrauterine extent, and lymph node status, all of which are essential in choosing the most appropriate therapy. Multiparametric MRI using a combination of T2-weighted sequences, diffusion-weighted imaging, and multiphase contrast-enhanced imaging is the mainstay for imaging assessment of endometrial cancer. Identification of important prognostic factors at MRI improves both treatment selection and posttreatment follow-up. MRI also plays a crucial role for fertility-preserving strategies and in patients who are not surgical candidates by helping guide therapy and identify procedural complications. This review is a product of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease-Focused Panel and reflects a multidisciplinary international collaborative effort to summarize updated information highlighting the role of MRI for endometrial cancer depiction and delineation, treatment planning, and follow-up. The article includes information regarding dedicated MRI protocols, tips for MRI reporting, imaging pitfalls, and strategies for image quality optimization. The roles of MRI-guided radiation therapy, hybrid PET/MRI, and advanced MRI techniques that are applicable to endometrial cancer imaging are also discussed. Online supplemental material is available for this article. ^©RSNA, 2022.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions

BACKGROUND

To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.

METHODS

One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.

RESULTS

Stage IA EC showed lower ADC_10th, ADC_90th, ADC_min, ADC_max, ADC_mean, ADC_median, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADC_median yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10^-3 mm²/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC_10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADC_min and ADC_10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).

CONCLUSIONS

Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.

Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia

Objectives

To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH).

Materials and Methods

A total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models.

Results

The combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900).

Conclusions

The radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.