Update on MRI in Evaluation and Treatment of Endometrial Cancer

Role of MRI in Assessing the Feasibility of Fertility-Sparing Treatments for Early-Stage Endometrial and Cervical Cancers

Fertility-sparing treatment (FST) has become a key aspect of managing gynecologic cancers in reproductive-age patients who wish to preserve fertility. Several leading clinical societies, including the European Society of Gynecological Oncology, the European Society for Radiotherapy and Oncology, the European Society of Pathology, and the European Society of Human Reproduction and Embryology, have recently published evidence-based guidelines on fertility-sparing strategies and posttreatment surveillance of patients with early-stage gynecologic cancers, in particular endometrial and cervical cancers. These guidelines highlight MRI as essential to initial patient selection and follow-up. Properly tailored pelvic MRI protocols and clear MRI reports are key to performing accurate staging, assessing eligibility, and confirming the initial and ongoing feasibility of FST. Accordingly, radiologists, particularly those specializing in gynecologic imaging, play a critical role in the multidisciplinary approach to FST. They should be well-versed in FST eligibility criteria and key MRI finding before and after FST, ensuring these details are comprehensively communicated in structured MRI reports.

Updates on Imaging of Common Urogenital Neoplasms

Urogenital neoplasms represent some of the most common malignancies [...].

Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database. 91 radiomics features were extracted from ADC maps and T1C images, and 566 pathomic features from cell detections and cell density maps at four different resolutions. Spearman's correlation and Bayes Factor analysis were used to evaluate radio-pathomic correlations. Significant cross-scale correlations were found, with strengths ranging from 0.57 to 0.89 in absolute value (9.47 × 104 < BF < 4.77 × 1014) for the ADC task, and from 0.64 and 0.70 (1.80 × 104 < BF < 5.69 × 105) for the T1C task. Most significant and high cross-scale associations were observed between ADC textural features and features from cell density maps. Correlations involving morphometric features and ADC and T1C first-order features were also observed, reflecting variations in tumor aggressiveness and tissue composition. These findings suggest that correlating radiomic features from ADC and T1C features with histopathological features can enhance understanding of EC intratumoral heterogeneity.

Development and Validation of a Nomogram Based on Multiparametric MRI for Predicting Lymph Node Metastasis in Endometrial Cancer: A Retrospective Cohort Study

RATIONALE AND OBJECTIVES

To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC).

MATERIALS AND METHODS

We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86). Based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) arterial phase and equilibrium phase images, radiomics features were extracted. Clinical characteristics were determined using multivariate logistic regression analysis. Subsequently, eight machine learning classification algorithms were employed to construct the radiomics model and clinical models, from which the best algorithm was selected. Ultimately, the radiomics and clinical features were combined to establish the radiomics nomogram. The efficacy of each model was appraised through receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The LR algorithm demonstrated superior predictive accuracy, with areas under the curve (AUCs) of 0.903 and 0.824 in the test and validation sets, respectively. Radiomics nomograms showed better predictive performance compared to clinical models or radiomics models, the AUCs in the test and external validation set were 0.900 (95% confidence interval [CI]: 0.784-1.000) and 0.858 (95%CI: 0.750-0.966), respectively. The calibration curve and DCA indicated that the nomogram had excellent predictive performance.

CONCLUSION

The nomogram based on radiomics features and clinical parameters could effectively predict LNM in patients with EC, thus providing a basis for clinicians to develop individualized treatment plans preoperatively.

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning

BACKGROUND

Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images.

PURPOSE

To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.

STUDY TYPE

Retrospective.

POPULATION

Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.

FIELD STRENGTH/SEQUENCE

T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system.

ASSESSMENT

EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort.

STATISTICAL TESTS

Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.

RESULTS

EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).

DATA CONCLUSION

The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Development of a nomogram based on whole-tumor multiparametric MRI histogram analysis to predict deep myometrial invasion in stage I endometrioid endometrial carcinoma preoperatively

BACKGROUND

The depth of myometrial invasion determines whether International Federation of Gynecology and Obstetrics stage I endometrioid endometrial carcinoma (EEC) patients undergo lymph node dissection. However, subjective evaluation results relying on magnetic resonance imaging (MRI) are not always satisfactory.

PURPOSE

To develop a nomogram based on whole-volume tumor MRI histogram parameters to preoperatively predict deep myometrial invasion (DMI) in patients with stage I EEC.

MATERIAL AND METHODS

This retrospective analysis included 131 EEC patients and a training/validation cohort of 92/39 patients at a 7:3 ratio. The histogram parameters were obtained from multiple sequences (ADC mapping and T2-weighted imaging) within volumes of interest. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used for feature selection. The performance of clinical model, histogram model, and histogram nomogram was evaluated by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS

Age and two morphological features (maximum anteroposterior tumor diameter on sagittal T2-weighted images [APsag] and the tumor area ratio [TAR]) were selected to construct the clinical model. Five histogram parameters were selected for the creation of the histogram model. The nomogram, which combines the histogram parameters, age, APsag, and TAR, achieved the highest AUCs in both the training and validation cohorts (nomogram vs. histogram vs. clinical model: 0.973 vs. 0.871 vs. 0.934 [training] and 0.972 vs. 0.870 vs. 0.928 [validation]).

CONCLUSION

The MR histogram nomogram can help predict the DMI of patients with stage I EEC preoperatively, assisting physicians in the development of personalized treatment strategies.

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P  < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P  < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

Construction and validation of a prediction model for preoperative prediction of Ki-67 expression in endometrial cancer patients by apparent diffusion coefficient

AIM

Ki-67 is a marker of cell proliferation and is increasingly being used as a primary outcome measure in preoperative window studies of endometrial cancer (EC). This study explored the feasibility of using apparent diffusion coefficient (ADC) values in noninvasive prediction of Ki-67 expression levels in EC patients before surgery, and constructs a nomogram by combining clinical data.

MATERIAL AND METHODS

This study retrospectively analyzed 280 EC patients who underwent preoperative magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in our hospital from January 2017 to February 2023. Evaluate the potential nonlinear relationship between ADC values and Ki-67 expression using the nomogram. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The results of this study showed a nonlinear correlation between ADCmin and Ki-67 expression (nonlinear P = 0.019), and the nonlinear correlation between ADCmean and Ki-67 expression (nonlinear P = 0.019). In addition, this study constructed the nomogram by incorporating ADCmax, International Federation of Gynecology and Obstetrics (FIGO), and chemotherapy. The area under the curve (AUC) values of the ROC for nomogram, ADCmax, FIGO, chemotherapy and grade in the training set were 0.783, 0.718, 0.579, 0.636, and 0.654, respectively. In the validation set, the AUC values for nomogram, ADCmax, FIGO, chemotherapy, and grade were 0.820, 0.746, 0.558, 0.542, and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model.

CONCLUSION

A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki-67 expression in EC patients before surgery.

Extracellular gadolinium-based contrast agents

Extracellular gadolinium-based contrast agents (GBCA) are commonly used in magnetic resonance imaging (MRI) because they increase the detection of alterations, improve tissue characterisation and enable a more precise differential diagnosis. GBCAs are considered to be safe but they are not risk-free. When using GBCAs, it is important to be aware of the risks and to know how to react in different situations (pregnancy, breastfeeding, kidney failure) including if complications occur (extravasations, adverse, allergic or anaphylactic reactions). The article describes the characteristics of the gadolinium molecule, the differences in the biochemical structure of these GBCA, their biodistribution and the effect on the MRI signal. It also reviews safety aspects and the most common clinical applications.

Time-synchronized 2-deoxy-2-[18F]fluoro-D-glucose PET/MRI with MR-active trigger and Bayesian penalized likelihood reconstruction: Diagnostic utility for locoregional extension of endometrial cancer

PURPOSE

Minimal misregistration of fused PET and MRI images can be achieved with simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI). However, the acquisition of multiple MRI sequences during a single PET emission scan may impair fusion precision of each sequence. This study evaluated the diagnostic utility of time-synchronized PET/MRI using an MR active trigger and a Bayesian penalized likelihood reconstruction algorithm (BPL) to assess the locoregional extension of endometrial cancer.

METHODS

Fifty-five patients with endometrial cancer who underwent pelvic 2-deoxy-2-[18F]fluoro-D-glucose PET/MRI were retrospectively evaluated. The PET emission time for the BPL reconstruction was determined by the MR active trigger of each MR sequence. The concordance rates of image interpretation with pathological T-staging, diagnostic performance for deep myometrial invasion (MI), and diagnostic confidence levels were evaluated by two readers and compared between time-synchronized, overlapping (conventional and simultaneous, but not time-synchronized), and sequential (not simultaneous) PET/MRI and MRI with diffusion-weighted imaging. Misregistration of the PET/MRI-fused images was determined by evaluating the differences in bladder dimensions.

RESULTS

The T classification by time-synchronized PET/MRI was the most concordant with the pathological T classification for the two readers. Time-synchronized PET/MRI had a significantly higher diagnostic performance for deep MI and higher confidence level scores than overlapping PET/MRI for the novice reader (p = 0.033 and p = 0.038, respectively). The differences in bladder dimension on sequential PET/MRI were significantly larger than those on overlapping and time-synchronized PET/MRI (p <0.001).

CONCLUSION

Time-synchronized PET/MRI is superior to conventional PET/MRI for assessing the locoregional extension of endometrial cancer.

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Diagnostic utility of apparent diffusion coefficient in preoperative assessment of endometrial cancer: are we ready for the 2023 FIGO staging?

BACKGROUND

Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC.

METHODS

Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results.

RESULTS

There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm2/s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association).

CONCLUSIONS

The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Label-free imaging diagnosis and collagen-optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy

The assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two-photon excited fluorescence intensity. Moreover, by utilizing semi-automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

Diagnostic dilemma in female genital tract tuberculosis: A case report

Female genital tract tuberculosis presents a diagnostic challenge because of its variable clinical presentation and radiological manifestation. Most patients are present with history of infertility, pain in the abdomen, vaginal discharge, and bleeding. These symptoms mimic those of gynecological cancer, such as endometrial carcinoma. Endometrial cancer typically manifests with vaginal bleeding in the post-menopausal age group; however, in less than 10% to 20% patients, it can occur in perimenopausal age groups, which makes it difficult to distinguish between malignancy and tuberculosis. We present a case report of a 40-year-old woman who complained of vaginal bleeding and lower abdominal pain. Her imaging findings favored the diagnosis of endometrial carcinoma but histopathology revealed tuberculosis.

A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study

BACKGROUND

Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.

METHODS

Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.

RESULTS

No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).

CONCLUSIONS

Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.

RadioGraphics Update: 2023 FIGO Staging System for Endometrial Cancer

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Evaluation of multiplexed sensitivity encoding diffusion-weighted imaging in detecting uterine lesions: Image quality optimization

PURPOSE

To compare the image quality of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and single-shot echo-planar imaging (SS-EPI-DWI) techniques in uterine MRI.

METHODS

Eighty-eight eligible patients underwent MUSE-DWI and SS-EPI-DWI examinations simultaneously using a 3.0 T MRI system. Two radiologists independently performed quantitative and qualitative analysis of the two groups of images using a double-blind method. The weighted Kappa test was used to evaluate the interobserver agreement. Wilcoxon's rank sum test was used for qualitative parameters, and paired t-test was used for quantitative parameters. Spearman rank correlation analysis was used to obtained correlation between pathological results and mean apparent diffusion coefficient (ADC) value.

RESULTS

The qualitative and quantitative analysis of the images by the two radiologists were in good or excellent agreement, with weighted kappa value ranging from 0.636 to 0.981. The scores of total subjective image quality (15.4 ± 0.99) and signal-to-noise ratio (158.99 ± 60.71) of MUSE-DWI were significantly higher than those of SS-EPI-DWI (12.93 ± 1.62 P < 0.001; 130.23 ± 48.29 P < 0.05). It effectively reduced image distortion and artifact, and had better lesion conspicuity. There was no significant difference in contrast-to-noise ratio score and average ADC values between the two DWI sequences. The average ADC values of the two DWI sequences were highest in the normal uterus group and lowest in the endometrial cancer group, with statistically significant differences among groups (P < 0.01). In addition, the average ADC values of the two DWI sequences were negatively correlated with the type of lesions, decreasing with the malignancy of the lesions (r = -0.805 P < 0.01, r = -0.815 P < 0.01).

CONCLUSION

Compared to SS-EPI-DWI, MUSE-DWI can significantly reduce distortion, artifacts, and fuzziness in MRI of uterine lesions, which is more conducive to lesion detection.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND

Endometrial cancer (EC) is a common gynecological malignancy that typically requires prompt surgical intervention; however, the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes. Previous studies have highlighted the prognostic potential of circulating tumor DNA (ctDNA) monitoring for minimal residual disease in patients with EC.

AIM

To develop and validate an optimized ctDNA-based model for predicting short-term postoperative EC recurrence.

METHODS

We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model, which was validated on 143 EC patients operated between 2020 and 2021. Prognostic factors were identified using univariate Cox, Lasso, and multivariate Cox regressions. A nomogram was created to predict the 1, 1.5, and 2-year recurrence-free survival (RFS). Model performance was assessed via receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA), leading to a recurrence risk stratification system.

RESULTS

Based on the regression analysis and the nomogram created, patients with postoperative ctDNA-negativity, postoperative carcinoembryonic antigen 125 (CA125) levels of < 19 U/mL, and grade G1 tumors had improved RFS after surgery. The nomogram's efficacy for recurrence prediction was confirmed through ROC analysis, calibration curves, and DCA methods, highlighting its high accuracy and clinical utility. Furthermore, using the nomogram, the patients were successfully classified into three risk subgroups.

CONCLUSION

The nomogram accurately predicted RFS after EC surgery at 1, 1.5, and 2 years. This model will help clinicians personalize treatments, stratify risks, and enhance clinical outcomes for patients with EC.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma

RATIONALE AND OBJECTIVES

To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA).

MATERIALS AND METHODS

Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared.

RESULTS

Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001).

CONCLUSION

The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.

Evaluation of multi-parameter MRI in preoperative staging of endometrial carcinoma

Background

Endometrial carcinoma (EC) is a prevalent gynecological malignancy, necessitating accurate preoperative staging for effective treatment planning. This study explores the application value of multi-parameter MRI in diagnosing and staging endometrial cancer.

Methods

Seventy-six patients diagnosed with endometrial cancer underwent 3.0 T pelvic MRI within two weeks before surgery. Imaging data were analyzed based on FIGO clinical staging criteria. The study assessed the sensitivity, specificity, positive predictive value, and negative predictive value of MRI for each stage.

Results

Postoperative pathology confirmed 71 cases of endometrial adenocarcinoma, 3 serous adenocarcinoma, and 2 clear cell carcinomas. MRI staging showed a high consistency (Kappa value = 0.786) with postoperative pathology. The overall accuracy of MRI diagnosis was 86.8%. Sensitivity and specificity varied for each stage: IA (91.3%, 96.2%), IB (88.6%, 93.8%), II (97.4%, 89.2%), and III (84.2%, 100%).

Conclusion

While there was a slight misdiagnosis rate, the overall accuracy of preoperative MRI for endometrial cancer was high, aiding in precise diagnosis and clinical staging. MRI effectively identified myometrial infiltration, cervical involvement, paracentral extension, and lymph node metastasis. Further research with larger sample sizes is recommended for enhanced reliability.

Endometrial Cancer: 2023 Revised FIGO Staging System and the Role of Imaging

The FIGO endometrial cancer staging system recently released updated guidance based on clinical evidence gathered after the previous version was published in 2009. Different imaging modalities are beneficial across various stages of endometrial cancer (EC) management. Additionally, ongoing research studies are aimed at improving imaging in EC. Gynecological cancer is a crucial element in the practice of a body radiologist. With a new staging system in place, it is important to address the role of radiology in the EC diagnostic pathway. This article is a comprehensive review of the changes made to the FIGO endometrial cancer staging system and the impact of imaging in the staging of this disease.

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).

PET/MRI in Endometrial Cancer: Imaging Biomarkers are Associated with Disease Progression and Overall Survival

RATIONALE AND OBJECTIVES

To evaluate the association between positron emission tomography (PET)/magnetic resonance imaging (MRI) biomarkers and survival outcomes in patients with endometrial cancer.

MATERIALS AND METHODS

Between April 2014 and April 2016, 88 patients with newly diagnosed endometrial cancer participated this prospective study and underwent [18F] fluorodeoxyglucose PET/MRI. Sixty-nine patients with measurable tumors on PET/MRI were included in the image analysis. Imaging biomarkers included the minimum and mean apparent diffusion coefficients (ADCmin and ADCmean), maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumors. The log-rank test and Cox proportional hazards model were used to assess the relationship between imaging biomarkers and survival.

RESULTS

After a median follow-up of 80 months, 15 (22%) patients had tumor progression and six (9%) patients died. The results of ADCmin, ADCmean, and SUVmax did not show a significant association with progression-free survival (PFS) and overall survival (OS). Significantly shorter PFS was noted in patients with primary tumors with higher MTV (P < 0.001) and TLG (P < 0.001). Significantly shorter OS was also noted in patients with primary tumors with higher MTV (P = 0.048) and TLG (P = 0.034). In the multivariate analysis, MTV was an independent predictor of PFS (hazard ratio = 10.84, P = 0.033).

CONCLUSION

PET/MRI biomarkers, particularly MTV and TLG, are associated with PFS and OS in patients with endometrial cancer. MTV was an independent predictor of PFS.

Predictive Value of Magnetic Resonance Imaging in Risk Stratification and Molecular Classification of Endometrial Cancer

This study evaluated the magnetic resonance imaging (MRI) findings of endometrial cancer (EC) patients and identified differences based on risk group and molecular classification. The study involved a total of 175 EC patients. The MRI data were retrospectively reviewed and compared based on the risk of recurrence. Additionally, the associations between imaging phenotypes and genomic signatures were assessed. The low-risk and non-low-risk groups (intermediate, high-intermediate, high, metastatic) showed significant differences in tumor diameter (p < 0.001), signal intensity and heterogeneity on diffusion-weighted imaging (DWI) (p = 0.003), deep myometrial invasion (involvement of more than 50% of the myometrium), cervical invasion (p < 0.001), extrauterine extension (p = 0.002), and lymphadenopathy (p = 0.003). Greater diffusion restriction and more heterogeneity on DWI were exhibited in the non-low-risk group than in the low-risk group. Deep myometrial invasion, cervical invasion, extrauterine extension, lymphadenopathy, recurrence, and stage discrepancy were more common in the non-low-risk group (p < 0.001). A significant difference in microsatellite stability status was observed in the heterogeneity of the contrast-enhanced T1-weighted images (p = 0.027). However, no significant differences were found in MRI parameters related to TP53 mutation. MRI features can be valuable predictors for differentiating risk groups in patients with EC. However, further investigations are needed to explore the imaging markers based on molecular classification.

Diagnostic accuracy of MRI for assessing lymphovascular space invasion in endometrial carcinoma: a meta-analysis

BACKGROUND

The lymphovascular space invasion (LVSI) status of endometrial cancer (EC) has guiding significance in lymph node dissection. However, LVSI can only be obtained after surgery. Researchers have tried to extract the information of LVSI using magnetic resonance imaging (MRI).

PURPOSE

To evaluate the ability of preoperative MRI to predict the LVSI status of EC.

MATERIAL AND METHODS

A search was conducted by using the PubMed/MEDLINE, EMBASE, Web of Science, and the Cochrane Library databases. Articles were included according to the criteria. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2. A bivariate random effects model was used to obtain pooled summary estimates, heterogeneity, and the area under the summary receiver operating characteristic curve (AUC). A subgroup analysis was performed to identify sources of heterogeneity.

RESULTS

A total of nine articles (814 patients) were included. The risk of bias was low or unclear for most studies, and the applicability concerns were low or unclear for all studies. The summary AUC values as well as pooled sensitivity and specificity of LVSI status in EC were 0.82, 73%, and 77%, respectively. According to the subgroup analysis, radiomics/non-radiomics features, country/region, sample size, age, MR manufacturer, magnetic field, scores of risk bias, and scores of applicability concern may have caused heterogeneity.

CONCLUSION

Our meta-analysis showed that MRI has moderate diagnostic efficacy for LVSI status in EC. Large-sample, uniformly designed studies are needed to verify the true value of MRI in assessing LVSI.

US, MRI, CT Performance and Interpretation of Uterine Masses

Uterine masses are commonly encountered as incidental findings during cross-sectional imaging or when individuals present with symptoms such as pain and bleeding. The World Health Organization categorizes tumors of the uterine corpus into 5 distinct groups: endometrial epithelial tumors and their precursors, tumor-like growths, mesenchymal uterine tumors, tumors with a combination of epithelial and mesenchymal elements, and various other types of tumors. The primary imaging method for assessing uterine abnormalities is transvaginal ultrasound. However, magnetic resonance imaging (MRI) can be employed to enhance the visualization of soft tissues, enabling a more detailed characterization of uterine masses. This article aims to outline the imaging features of both benign and malignant uterine masses using ultrasound, MRI, and computed tomography.

Value of ultrasound and magnetic resonance imaging combined with tumor markers in the diagnosis of ovarian tumors

BACKGROUND

Compare the diagnostic performance of ultrasound (US), magnetic resonance imaging (MRI), and serum tumor markers alone or in combination for detecting ovarian tumors.

AIM

To investigate the diagnostic value of US, MRI combined with tumor markers in ovarian tumors.

METHODS

The data of 110 patients with ovarian tumors, confirmed by surgery and pathology, were collected in our hospital from February 2018 to May 2023. The dataset included 60 cases of benign tumors and 50 cases of malignant tumors. Prior to surgery, all patients underwent preoperative US and MRI examinations, as well as serum tumor marker tests [carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4)]. The aim of the study was to compare the diagnostic performance of these three methods individually and in combination for ovarian tumors.

RESULTS

This study found statistically significant differences in the ultrasonic imaging characteristics between benign and malignant tumors. These differences include echo characteristics, presence or absence of a capsule, blood flow resistance index, clear tumor shape, and blood flow signal display rate (P < 0.05). The apparent diffusion coefficient values of the solid and cystic parts in benign tumors were found to be higher compared to malignant tumors (P < 0.05). Additionally, the time-intensity curve image features of benign and malignant tumors showed significant statistical differences (P < 0.05). The levels of serum CA125 and HE4 in benign tumors were lower than those in malignant tumors (P < 0.05). The combined use of US, MRI, and tumor markers in the diagnosis of ovarian tumors demonstrates higher accuracy, sensitivity, and specificity compared to using each method individually (P < 0.05).

CONCLUSION

US, MRI, and tumor markers each have their own advantages and disadvantages when it comes to diagnosing ovarian tumors. However, by combining these three methods, we can significantly enhance the accuracy of ovarian tumor diagnosis, enabling early detection and identification of the tumor’s nature, and providing valuable guidance for clinical treatment.

The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review

(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.

Added Value of FDG PET/MRI in Gynecologic Oncology: A Pictorial Review

Fluorine 18-fluorodeoxyglucose (FDG) PET and MRI independently play a valuable role in the management of patients with gynecologic malignancies, particularly endometrial and cervical cancer. The PET/MRI hybrid imaging technique combines the metabolic information obtained from PET with the excellent soft-tissue resolution and anatomic details provided by MRI in a single examination. MRI is the modality of choice for assessment of local tumor extent in the pelvis, whereas PET is used to assess for local-regional spread and distant metastases. The authors discuss the added value of FDG PET/MRI in imaging gynecologic malignancies of the pelvis, with a focus on the role of FDG PET/MRI in diagnosis, staging, assessing treatment response, and characterizing complications. PET/MRI allows better localization and demarcation of the extent of disease, characterization of lesions and involvement of adjacent organs and lymph nodes, and improved differentiation of benign from malignant tissues, as well as detection of the presence of distant metastasis. It also has the advantages of decreased radiation dose and a higher signal-to-noise ratio of a prolonged PET examination of the pelvis contemporaneous with MRI. The authors provide a brief technical overview of PET/MRI, highlight how simultaneously performed PET/MRI can improve stand-alone MRI and PET/CT in gynecologic malignancies, provide an image-rich review to illustrate practical and clinically relevant applications of this imaging technique, and review common pitfalls encountered in clinical practice. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Image-Guided Radiotherapy for Gynecologic Malignancies: What the Radiologist Needs to Know

Pelvic imaging is integral to contemporary radiotherapy (RT) management of gynecologic malignancies. For cervical, endometrial, vulvar, and vaginal cancers, three-dimensional imaging modalities aid in tumor staging and RT candidate selection and inform treatment strategy, including RT planning, execution, and posttherapy surveillance. State-of-the-art care routinely incorporates magnetic resonance (MR) imaging, 18F-fluorodeoxyglucose-PET/computed tomography (CT), and CT to guide external beam RT and brachytherapy, allowing the customization of RT plans to maximize patient outcomes and reduce treatment-related toxicities. Follow-up imaging identifies radiation-resistant and recurrent disease as well as short-term and long-term toxicities from RT.

PET/MRI in Gynecologic Malignancy

Patients with gynecologic malignancies often require a multimodality imaging approach for initial staging, treatment response assessment, and surveillance. MRI imaging and PET are two well-established and widely accepted modalities in this setting. Although PET and MRI imaging are often acquired separately on two platforms (a PET/computed tomography [CT] and an MRI imaging scanner), hybrid PET/MRI scanners offer the potential for comprehensive disease assessment in one visit. Gynecologic malignancies have been one of the most successful areas for implementation of PET/MRI. This article provides an overview of the role of this platform in the care of patients with gynecologic malignancies.

The value of serum HE4 and CA125 levels for monitoring the recurrence and risk stratification of endometrial endometrioid carcinoma

To evaluate the role of serum human epididymis secretory protein 4 (HE4) and carbohydrate antigen 125 (CA125) levels for predicting and monitoring the recurrence of endometrial endometrioid carcinoma (EEC) and assessing preoperative risk stratification in EEC patients. A total of 434 EEC patients were selected for this retrospective study between May 2011 and August 2018. Serum HE4 and CA125 levels were analyzed before the initial treatment, at the first postoperative follow-up, and at recurrence or the last follow-up. Patients were risk stratified according to the European Society for Medical Oncology (ESMO), European Society for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guideline. We compared the ability of these biomarkers for prediction and monitoring by performing receiver operating characteristic curve analysis and identified optimal cut-off values by determining the Youden index. Kaplan-Meier analyses were also performed to determine prognostic value. Preoperative serum HE4 was identified as a significant predictor for the recurrence of EEC (p = 0.014). Preoperative serum HE4 and CA125 levels were related to depth of myometrial invasion, lymph node status and FIGO stage. Serum HE4 and CA125 levels were both statistically significant markers for monitoring the recurrence of EEC (P = 0.000 for each biomarker). When combined, the two markers showed higher levels of sensitivity and specificity. The two biomarkers were also significant biomarkers for evaluating the risk stratification of patients undergoing lymphadenectomy (P = 0.000 for each biomarker). For premenopausal stage I patients, preoperative serum HE4 and CA125 levels were significant predictors of the need for ovarian preservation (P = 0.000 and P = 0.002, respectively). For premenopausal patients with stage I intramucosal differentiation, preoperative serum levels of HE4 were significant predictors for fertility preservation (P = 0.024). Preoperative serum HE4 level can be used to predict the recurrence of EEC. Postoperative serum HE4 and CA125 levels can be used to monitor the recurrence of EEC and are more sensitive when combined. Preoperative serum levels of CA125 and HE4 levels are of significant value for risk stratification in EEC patients.

Imaging of Endometrial Cancer

Endometrial cancer is the most common gynecologic cancer in the United States and Europe, with an increasing incidence rate in high-income countries. MR imaging is recommended for treatment planning because it provides critical information on the extent of myometrial and cervical invasion, extrauterine spread, and lymph node status, all of which are important in the selection of the most appropriate therapy. This article highlights the added value of imaging, focused on MR imaging, in the assessment of endometrial cancer and summarizes the role of MR imaging for endometrial cancer risk stratification and management.