OBJECTIVE: This study investigates the differences in diagnostic performance between diffuse-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE), either alone or in combination with T2-weighted imaging (T2WI), for diagnosing deep myometrial invasion (dMI) of endometrial cancers (EC).
METHODS: We performed a comprehensive search for published studies comparing DWI and DCE for preoperatively diagnosing dMI of EC. The overall diagnostic accuracy of each test was calculated using the areas under the summary receiver operating characteristic curves (AUCs). The sensitivities and specificities were compared using bivariate meta-regression.
RESULTS: Pooled analysis of nineteen studies with 961 patients (main group) showed that DWI had a larger AUC (0.943, 95% confidence interval (CI) = 0.921-0.967) than DCE (0.922, 95% CI = 0.893-0.953). For the subgroup comprising 7 studies, DWI combined with T2WI and DCE combined with T2WI showed AUCs of 0.959 (95% CI, 0.932-0.986) and 0.929 (95% CI, 0.847-1.000), respectively. None of the differences in AUCs were statistically significant. All comparisons of the sensitivities and specificities of the main group and subgroup also showed no significant differences.
CONCLUSIONS: This meta-analysis found no significant difference in diagnostic performance between DWI and DCE for diagnosis of dMI in EC. DWI may be preferred for its ease of use in clinical practice.

The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying deep myometrial invasion (DMI) using MRI. Moreover, these existing methods exhibit relatively unsatisfactory sensitivity and specificity. This study proposes a novel computerized method to facilitate the accurate detection of DMI on MRI. This method requires only the corpus uteri region provided by humans or computers instead of the tumor region. We also propose a geometric feature called LS to describe the irregularity of the tissue structure inside the corpus uteri triggered by EC, which has not been leveraged for the DMI prediction model in other studies. Texture features are extracted and then automatically selected by recursive feature elimination. Utilizing a feature fusion strategy of strong and weak features devised in this study, multiple probabilistic support vector machines incorporate LS and texture features, which are then merged to form the ensemble model EPSVM. The model performance is evaluated via leave-one-out cross-validation. We make the following comparisons, EPSVM versus the commonly used classifiers such as random forest, logistic regression, and naive Bayes; EPSVM versus the models using LS or texture features alone. The results show that EPSVM attains an accuracy, sensitivity, specificity, and F1 score of 93.7%, 94.7%, 93.3%, and 87.8%, all of which are higher than those of the commonly used classifiers and the models using LS or texture features alone. Compared with the methods in existing studies, EPSVM exhibits high performance in terms of both sensitivity and specificity. Moreover, LS can achieve an accuracy, sensitivity, and specificity of 89.9%, 89.5%, and 90.0%. Thus, the devised geometric feature LS is significant for DMI detection. The fusion of LS and texture features in the proposed EPSVM can provide more reliable prediction. The computer-aided classification based on the proposed method can assist radiologists in accurately identifying DMI on MRI.

Deep myometrial invasion (≥50%) is a prognostic factor for lymph node metastases and decreased survival in endometrial cancer. There is no consensus regarding which pre/intraoperative diagnostic method should be preferred. Our aim was to explore the pattern of diagnostic methods for myometrial invasion assessment in Sweden and to evaluate differences among magnetic resonance imaging (MRI), transvaginal sonography, frozen section, and gross examination in clinical practice.
This is a nationwide historical cohort study; women with endometrial cancer with data on assessment of myometrial invasion and FIGO stage I-III registered in the Swedish Quality Registry for Gynecologic Cancer (SQRGC) between 2017 and 2019 were eligible. Data on age, histology, FIGO stage, method, and results of myometrial invasion assessment, pathology results, and hospital level were collected from the SQRGC. The final assessment by the pathologist was considered the reference standard.
In the study population of 1401 women, 32% (n = 448) had myometrial invasion of 50% of more. The methods reported for myometrial invasion assessment were transvaginal sonography in 59%, MRI in 28%, gross examination in 8% and frozen section in 5% of cases. Only minor differences were found for age and FIGO stage when comparing methods applied for myometrial invasion assessment. The sensitivity, specificity, and accuracy to find myometrial invasion of 50% or more with transvaginal sonography were 65.6%, 80.3%, and 75.8%, for MRI they were 76.9%, 71.9%, and 73.8%, for gross examination they were 71.9%, 93.6%, and 87.3%, and for frozen section they were 90.0%, 92.7%, and 92.0%, respectively.
In Sweden, the assessment of deep myometrial invasion is most often performed with transvaginal sonography, but the sensitivity is lower than for the other diagnostic methods. In clinical practice, the accuracy is moderate for transvaginal sonography and MRI.

OBJECTIVE: The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol.
METHODS: In this multicenter retrospective study, MRI were independently evaluated by 4 radiologists (2 senior and 2 novice) with a sequence-based approach to identify DMI. The performance of the entire protocol was also evaluated. A comparison between the different sequences assessed by the same reader was performed using receiver operating curve and post-hoc analysis. Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-observer variability.
RESULTS: A total of 92 patients were included. The performance of the readers did not show significant differences among DWI, DCE and the entire protocol. For only one senior radiologist, who reached the highest diagnostic accuracy with the entire protocol (82,6 %), both DWI (p = 0,0197) and entire protocol (p = 0,0039) were found significantly superior to T2-w. The highest inter-observer agreement was obtained with the entire protocol by expert readers (ICC = 0,77).
CONCLUSIONS: For the detection of DMI, the performances of DWI and DCE alone and that of a complete protocol do not significantly differ, even though the latter ensures the highest reliability particularly for expert readers. In cases in which T2-w and DWI are consistent, an unenhanced protocol could be proposed.

To evaluate an MRI radiomics-powered machine learning (ML) model\'s performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability.
Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars\'s test was employed to compare the two readings.
Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48).
We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.

UNASSIGNED: We investigated the efficacy of circulating biomarkers together with histological grade and age to predict deep myometrial invasion (dMI) in endometrial cancer patients.
UNASSIGNED: HE4ren was developed adjusting HE4 serum levels towards decreased glomerular filtration rate as quantified by the eGFR-EPI formula. Preoperative HE4, HE4ren, CA125, age, and grade were evaluated in the context of perioperative depth of myometrial invasion in endometrial cancer (EC) patients. Continuous and categorized models were developed by binary logistic regression for any-grade and for G1-or-G2 patients based on single-institution data from 120 EC patients and validated against multicentric data from 379 EC patients.
UNASSIGNED: In non-cancer individuals, serum HE4 levels increase log-linearly with reduced glomerular filtration of eGFR ≤ 90 ml/min/1.73 m2. HE4ren, adjusting HE4 serum levels to decreased eGFR, was calculated as follows: HE4ren = exp[ln(HE4) + 2.182 × (eGFR-90) × 10-2]. Serum HE4 but not HE4ren is correlated with age. Model with continuous HE4ren, age, and grade predicted dMI in G1-or-G2 EC patients with AUC = 0.833 and AUC = 0.715, respectively, in two validation sets. In a simplified categorical model for G1-or-G2 patients, risk factors were determined as grade 2, HE4ren ≥ 45 pmol/l, CA125 ≥ 35 U/ml, and age ≥ 60. Cumulation of weighted risk factors enabled classification of EC patients to low-risk or high-risk for dMI.
UNASSIGNED: We have introduced the HE4ren formula, adjusting serum HE4 levels to reduced eGFR that enables quantification of time-dependent changes in HE4 production and elimination irrespective of age and renal function in women. Utilizing HE4ren improves performance of biomarker-based models for prediction of dMI in endometrial cancer patients.

OBJECTIVE: To investigate the added value of secondary reports issued by radiologists subspecializing in gynaecologic imaging for determining deep myometrial invasion of endometrial cancer on MRI.
METHODS: Initial (from referring institutions) and secondary (by subspecialized radiologists) interpretations of MRI of 55 patients with endometrial cancer were retrospectively reviewed. A radiologist blinded to clinicopathological information assessed both reports for the presence of deep myometrial invasion. Reference standard was based on hysterectomy specimens. Kappa coefficients (k) were used to measure their concordance. McNemar testing and receiver operating characteristic (ROC) analysis was used to compare sensitivities, specificities and areas under the curves (AUCs).
RESULTS: Deep myometrial invasion was present in 25 (45.5 %) patients. Among 27.3 % (15/55; k = 0.458) patients with discrepant results, secondary interpretations were correct in 10 (66.7 %) cases. Sensitivity was higher in secondary than in initial reports (76.0 % vs. 48.0 %, p = 0.039) while no significant difference was seen in specificity (70.0 % vs. 76.7 %, p = 0.668). At ROC analysis, there was a tendency for higher AUCs in secondary reports (0.785 vs 0.669, p = 0.096).
CONCLUSIONS: Secondary readings of MRI by subspecialized gynaecologic oncologic radiologists may provide incremental value in determining deep myometrial invasion of endometrial cancer.
CONCLUSIONS: • Deep myometrial invasion is an important prognostic factor in endometrial cancer. • Assessment of deep myometrial invasion is often discrepant between initial and secondary reports. • Secondary reports showed higher sensitivity and accuracy. • Secondary review of MRI may provide incremental value in endometrial cancer patients.