Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer.

Background Pathologic lymphovascular space invasion (LVSI) is associated with poor outcome in endometrial cancer. Its relationship with tumor stiffness, which can be measured with use of MR elastography, has not been extensively explored. Purpose To assess whether MR elastography-based mechanical characteristics can aid in the noninvasive prediction of LVSI in patients with endometrial cancer. Materials and Methods This prospective study included consecutive adult patients with a suspected uterine tumor who underwent MRI and MR elastography between October 2022 and July 2023. A region of interest delineated on T2-weighted magnitude images was duplicated on MR elastography images and used to calculate c (stiffness in meters per second) and φ (viscosity in radians) values. Pathologic assessment of hysterectomy specimens for LVSI served as the reference standard. Data were compared between LVSI-positive and -negative groups with use of the Mann-Whitney U test. Multivariable logistic regression was used to determine variables associated with LVSI positivity and develop diagnostic models for predicting LVSI. Model performance was assessed with use of area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results A total of 101 participants were included, 72 who were LVSI-negative (median age, 53 years [IQR, 48-62 years]) and 29 who were LVSI-positive (median age, 54 years [IQR, 49-60 years]). The tumor stiffness in the LVSI-positive group was higher than in the LVSI-negative group (median, 4.1 m/sec [IQR, 3.2-4.6 m/sec] vs 2.2 m/sec [IQR, 2.0-2.8 m/sec]; P < .001). Tumor volume, cancer antigen 125 level, and tumor stiffness were associated with LVSI positivity (adjusted odds ratio range, 1.01-9.06; P range, <.001-.04). The combined model (AUC, 0.93) showed better performance for predicting LVSI compared with clinical-radiologic model (AUC, 0.77; P = .003) and similar performance to the MR elastography-based model (AUC, 0.89; P = .06). Conclusion The addition of tumor stiffness as measured at MR elastography into a clinical-radiologic model improved prediction of LVSI in patients with endometrial cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ehman in this issue.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Clinicopathological Pattern of Endometrial Specimens in Women with Abnormal Uterine Bleeding and Ultrasonography Correlation.

BACKGROUND: Abnormal uterine bleeding (AUB) refers to any symptomatic deviation from normal menstruation. AUB is a common gynecological disorder in non-pregnant women of reproductive age, accounting for approximately 33% of gynecological outpatient visits. The early diagnosis and management cause of AUB is important because of increased incidence of endometrial carcinoma with rapid growth. Transvaginal ultrasound is non-invasive imaging technique used to find endometrial carcinoma before referring patients for invasive techniques. Dilatation and curettage (D&C) and endometrial biopsy are surgical procedures that scrape the endometrial lining of the uterus for diagnosis and treatment. The aim of this study is to describe the clinicopathologic pattern of endometrial specimens in women with AUB and ultrasonographic correlation. METHODS: Tissues from endometrial biopsy and curettage of 411 patients with AUB who referred to Shahid Mohammadi hospital were prospectively selected from 2021 to 2023. Patients were divided into three groups based on age and menstrual status including: premenopausal (18-39 years), perimenopausal (40-49 years) and postmenopausal (≥50 years). The results were correlated to patient's age and other data and evaluated with statistical analysis. RESULTS: During the two-year study period, a total of 411 endometrial specimens with clinical diagnosis of AUB were submitted and the results were analyzed. The youngest patient presenting with AUB was 21 years old, while the oldest was 77 years old. The most common complaint was menorrhagia in 201 (48.0%) out of 411 patients. The most common pathology finding in three groups was polyp in 100 (24.3%) cases. Hormonal effect was the next commonly observed pattern seen in 70 (17.0%) cases. P value was calculated as 0.003 which was significant using chi-square for the trend seen in age. CONCLUSION: Endometrial sampling is a useful tool for evaluation of women with AUB and referring patients for treatment. Histopathological evaluation of the endometrium is very useful in detecting the etiology of AUB. Transvaginal sonography has high sensitivity in detecting polyps.

The volumetric ADC histogram analysis in differentiating stage IA endometrial carcinoma from endometrial polyp.

OBJECTIVE: This study aimed to explore the value of apparent diffusion coefficient (ADC) histogram based on whole lesion volume in distinguishing stage IA endometrial carcinoma from the endometrial polyp. METHODS: MRI of 108 patients with endometrial lesions confirmed by pathology were retrospectively analysed, including 65 cases of stage IA endometrial carcinoma and 43 cases of endometrial polyp. The volumetric ADC histogram metrics and general imaging features were evaluated and measured simultaneously. All the features were compared between the 2 groups. The receiver operating characteristic curve was utilized to evaluate the diagnostic performance. RESULTS: The mean, max, min, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values of endometrial carcinoma were significantly lower than that of polyp (all P < .05). The skewness and kurtosis of ADC values in the endometrial carcinoma group were significantly higher than those in the endometrial polyp group, and the variance of ADC values in the endometrial carcinoma group was lower than those in the endometrial polyp group (all P < .05). Endometrial carcinoma demonstrated more obvious myometrial invasion combined with intralesion haemorrhage than polyp (all P < .05). The 25th percentile of ADC values achieved the largest areas under the curve (0.861) among all the ADC histogram metrics and general imaging features, and the sensitivity and specificity were 83.08% and 76.74%, with the cut-off value of 1.01 × 10-3 mm2/s. CONCLUSION: The volumetric ADC histogram analysis was an effective method in differentiating endometrial carcinoma from an endometrial polyp. The 25th percentile of ADC values has satisfactory performance for detecting malignancy in the endometrium. ADVANCES IN KNOWLEDGE: The ADC histogram metric based on whole lesion is a promising imaging-maker in differentiating endometrial benign and malignant lesions.

An evaluation of the utility of computed tomography in high-risk endometrial cancer surveillance.

OBJECTIVES: Endometrial cancer is a collection of heterogeneous histologies and molecular subtypes with different risk profiles. High-risk endometrial cancer surveillance regimens vary amongst providers. The National Comprehensive Cancer Network (NCCN) recommends symptom and exam-based surveillance for all endometrial cancers after remission, regardless of cancer stage and histology. Our objective was to identify the first method of detection of recurrence in high-risk endometrial cancers and examine disease recurrence and treatment patterns. METHODS: A retrospective review of patients diagnosed with high-risk endometrial cancer between November 2013 and February 2020 was conducted at a large academic institution. High-risk endometrial cancers were classified by histology and pathologic stage and were categorized by primary method of detection. RESULTS: Two hundred and twenty-nine patients were identified with high-risk endometrial cancer, 63 (28 %) of whom had a recurrence. Most recurrences were first detected with routine imaging in 31 patients (49.2 %) and symptom surveillance in 24 patients (38.15 %). Regardless of the detection method, most patients underwent systemic treatment. The average survival after recurrence was 2.0 years in the imaging cohort and 1.6 years in the non-imaging surveillance cohort. CONCLUSIONS: The most common site of recurrence in our cohort of high-risk endometrial cancer was in the lung, and most recurrences were identified with asymptomatic imaging. Though there was no statistically significant difference between the survival of those who underwent imaging surveillance vs. standard of care, there was a trend toward survival that deems further exploration with a larger cohort.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

Preoperative evaluation and a nomogram prediction model for pelvic lymph node metastasis in endometrial cancer.

OBJECTIVE: The primary objective of this study is to explore the preoperative risk factors of pelvic lymph node metastasis (PLNM) in endometrial cancer patients, and construct a nomogram prediction model. MATERIALS AND METHODS: We retrospectively collected various preoperative clinical characteristics of patients and analyzed their relationship with PLNM. Logistic regression analysis was used to screen for independent risk factors for PLNM of endometrial cancer. A nomogram prediction model was constructed, the receiver operating characteristic (ROC), calibration curve and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit. RESULTS: Out of the 276 patients, 74 (26.81%) with postoperative pathological confirmation of PLNM. Multivariate logistic regressive analysis demonstrated that preoperative depth of myometrial invasion (DIM) ≥50% determined by Magnetic Resonance Imaging (MRI) (p = 0.003), carbohydrate antigen 125 (CA125) (p = 0.030), carbohydrate antigen 19-9 (CA 19-9) (p = 0.044), and platelet/lymphocyte ratio (PLR) (p = 0.025) could serve as independent risk factors for PLNM. A risk factors-based nomogram prediction model was constructed, which showed good discrimination (AUC = 0.841, p < 0.001) and good efficacy (C-index = 0.842) and good calibration (mean absolute error = 0.046). DCA showed that the model can provide clinical benefits. CONCLUSIONS: Preoperative DIM ≥50% determined by MRI, serum CA 19-9, CA125 and PLR could be utilized to predict PLNM in endometrial cancer patients. This nomogram prediction model can provide preoperative help for evaluation and identification of patients with endometrial cancer, and provide a theoretical basis for clinical intervention.

Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI.

OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

OBJECTIVE: To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. METHODS: Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. RESULTS: In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). CONCLUSIONS: The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Accuracy of MRI in predicting deep myometrial invasion in endometrial cancer and the influence of leiomyoma, adenomyosis and the microcystic elongated and fragmented tumour pattern.

INTRODUCTION: The most common form of endometrial cancer is Type 1 endometrioid adenocarcinoma. Depth of myometrial invasion is the most important prognostic factor correlating with overall patient survival. The objective was to investigate how accurate magnetic resonance imaging (MRI) is in predicting the depth of myometrial invasion in preoperative assessment, and the influence of leiomyoma and/or adenomyosis, or microcystic, elongated and fragmented (MELF) pattern of invasion on MRI diagnostic performance. METHOD: Retrospective audit of 235 endometrial cancer patients from the regional Gynaecology Oncology multidisciplinary meeting at Auckland City Hospital, between January 2020 and January 2021. Radiologist assigned stage was compared to histopathology. Presence of leiomyoma, adenomyosis and MELF pattern evaluated followed by analysis under a Biostatistician's supervision. RESULTS: Overall MRI diagnostic accuracy for depth of myometrial invasion was 86%. For deep myometrial invasion, MRI had a sensitivity of 72% and specificity 91%. Out of the misreported 32/235 cases, 16 demonstrated fibroids and/or adenomyosis leading to a sensitivity of 57% and specificity 93% for deep invasion, compared with 94% and 74% respectively in the population without, demonstrating statistical significance. Thirty seven cases with MELF pattern of invasion showed a sensitivity of 81% and specificity 80% for deep invasion, compared with 63% and 92% respectively in the group without, demonstrating no statistical significance. CONCLUSION: MRI assessment of the depth of myometrial invasion in endometrial cancer has high accuracy. In the presence of background uterine fibroids/adenomyosis, pre-operative MRI accuracy of evaluating deep invasion shows a statistically significant reduction.

18F-FDG PET/CT Findings of Tibia Metastasis From Endometrial Adenocarcinoma.

ABSTRACT: Bone metastases from endometrial carcinoma are rare, especially when the bone is the sole metastatic site. A 55-year-old woman with a history of endometrial carcinoma was referred for FGD PET/CT scan due to pain in the left knee. The images showed that multiple lesions with intense activity were detected in the left tibia. Histopathological examination and immunohistochemistry of the left tibial lesion confirmed metastases from the endometrial adenocarcinoma.

Identifying safe diagnostic algorithms for sentinel lymph node mapping in high-risk endometrial cancer: The SENTIREC-endo study.

INTRODUCTION: It is unclear if sentinel node (SLN) mapping can replace pelvic- (PLD) and paraaortic lymphadenectomy (PALD) for high-risk endometrial cancer (EC). A diagnostically safe surgical algorithm, taking failed mapping cases into account, is not defined. We aimed to investigate the diagnostic accuracy of SLN mapping algorithms in women with exclusively high-risk EC. METHODS: We undertook a prospective national diagnostic cohort study of SLN mapping in women with high-risk EC from March 2017 to January 2023. The power calculation was based on the negative predictive value (NPV). Women underwent SLN mapping, PLD and PALD besides removal of suspicious and any FDG/PET-positive lymph nodes. Accuracy analyses were performed for five algorithms. RESULTS: 170/216 included women underwent SLN mapping, PLD and PALD and were included in accuracy analyses. 42/170 (24.7%) had nodal metastasis. The algorithm SLN and PLD in case of failed mapping, demonstrated a sensitivity of 86% (95% CI 74-100) and an NPV of 96% (95% CI 91-100). The sensitivity increased to 93% (95% CI 83-100) and the NPV to 98% (95% CI 94-100) if PLD was combined with removal of any PET-positive lymph nodes. Equivalent results were obtained if PLD and PALD were performed in non-mapping cases; sensitivity 93% (95% CI 83-100) and NPV 98% (95% CI 95-100). CONCLUSION: SLN-mapping is a safe staging procedure in women with high-risk EC if strictly adhering to a surgical algorithm including removal of any PET-positive lymph nodes independent of location and PLD or PLD and PALD in case of failed mapping.

Ultrasound finding of endometrial polyp and factors increasing risk of malignancy.

This article presents a comprehensive review of factors that increase the risk of malignancy in ultrasound findings of an endometrial polyp. We collected original studies, reviews, and meta-analyses that dealt with the topic of endometrial polyps and the risk of developing endometrial cancer. Each presumed risk factor was analysed individually. According to searched studies, abnormal uterine bleeding, old age, and body mass index are valid risk factors for developing endometrial cancer in endometrial polyps. Lynch syndrome patients are also in a high-risk group for endometrial cancer. On the other hand, the number of polyps, their size, diabetes mellitus, hypertension, and positive family history are factors with inconclusive results. There are either not enough data or different results among several studies.

DSFF-GAN: A novel stain transfer network for generating immunohistochemical image of endometrial cancer.

Immunohistochemistry (IHC) is a commonly used histological examination technique. Compared to Hematoxylin and Eosin (H&E) staining, it enables the examination of protein expression and localization in tissues, which is valuable for cancer treatment and prognosis assessment, such as the detection and diagnosis of endometrial cancer. However, IHC involves multiple staining steps, is time-consuming and expensive. One potential solution is to utilize deep learning networks to generate corresponding virtual IHC images from H&E images. However, the similarity of the IHC image generated by the existing methods needs to be further improved. In this work, we propose a novel dual-scale feature fusion (DSFF) generative adversarial network named DSFF-GAN, which comprises a cycle structure-color similarity loss, and DSFF block to constrain the model's training process and enhance its stain transfer capability. In addition, our method incorporates labeling information of positive cell regions as prior knowledge into the network to further improve the evaluation metrics. We train and test our model using endometrial cancer and publicly available breast cancer IHC datasets, and compare it with state-of-the-art methods. Compared to previous methods, our model demonstrates significant improvements in most evaluation metrics on both datasets. The research results show that our method further improves the quality of image generation and has potential value for the future clinical application of virtual IHC images.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.

PURPOSE: We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS: Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS: Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION: The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

The role of shear wave elastography in predicting endometrial cancer in patients presenting with abnormal uterine bleeding.

OBJECTIVE: Shear Wave Elastography (SWE) is an objective quantitative ultrasound elastography technique that can demonstrate the stiffness of anatomical structures to aid in their detection and characterization. We aimed to evaluate the role of shear wave elastography in differentiating endometrial carcinoma from benign uterine pathologies in women with abnormal uterine bleeding. PATIENTS AND METHODS: This prospective study was conducted at our institution from January 2020 to April 2020. A hundred patients with endometrial sampling planned and SWE due to abnormal uterine bleeding were included in the study. According to the histopathological results of the patients, those with normal and atrophic endometrium results were defined as group I (control group), those with benign results such as polyps and endometrial hyperplasia were defined as group II, and those with endometrial cancers were defined as group III. RESULTS: After adjustment for age, a statistically significant difference was found in Emean (mean and adjusted mean) value between the study groups (F2.96=86.37, p<.001, η2=0.64). The post-hoc analysis was performed with a Bonferroni adjustment. The mean Emean value was found to be statistically significantly higher in group III (17.14±0.40) compared to group I (10.39±0.26) and group II (11.49±0.32) (p<0.001). In addition, a statistically significant difference was found between the benign and normal groups. CONCLUSIONS: As a new diagnostic technique in gynecology, elastography appears to be a valuable tool in differentiating malign endometrial pathologies from normal or benign endometrial pathologies in females with abnormal uterine bleeding.

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer.

PURPOSE: To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). METHODS: Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. RESULTS: This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). CONCLUSION: A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.