MRI of endometrium cancer - how we do it

Diagnostic accuracy of TVUS and MRI in the preoperative evaluation of myometrial infiltration in patients with endometrial cancer: A meta-analysis

AIM

The incidence of endometrial cancer is on the rise worldwide. Accurate preoperative evaluation of myometrial infiltration is crucial for developing treatment strategies. This study compares the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) for myometrial infiltration (MI) in endometrial cancer (EC).

MATERIALS AND METHODS

We performed a systematic review and meta-analysis of observational studies, identified by screening Web of Science, SCOPUS, MEDLINE, PubMed, Google Scholar, and EMBASE databases. Studies published between January 1964 and June 2024 comparing the diagnostic accuracy of TVUS and MRI for MI were included. The data analysis focused on sensitivity, specificity, and overall diagnostic accuracy.

RESULTS

Twenty-two studies in EC patients were included. The diagnostic odds ratio (OR) for TVUS and for MRI was 18 (95% CI: 22-26) and 20 (95% CI: 14-28), respectively. TVUS was associated with a sensitivity and specificity of 76% (95% CI: 72-82%) and 84% (95% CI: 79-88%), respectively, while MRI had a sensitivity and specificity of 79% (95% CI: 73-84%) and 84% (95% CI: 80-88%), respectively. The area under the receiver operating characteristic curve (AUCROC) was 0.88 for TVUS and 0.89 for MRI. No significant publication bias was detected.

CONCLUSIONS

Both TVUS and MRI demonstrated comparable diagnostic accuracy for the preoperative evaluation of MI in EC.

Optimizing Final Pathology Determination in Endometrial Cancer: The Role of PET/CT, MRI, and Biopsy in Serous, Mixed Cell, Clear Cell, and Grade 3 Endometrioid Subtypes

Background: Accurate and timely diagnosis of endometrial cancer is crucial for guiding effective treatment and improving patient survival. Endometrial cancer diagnosis, staging, metastasis detection, and treatment planning utilize endometrial biopsy, magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT) scanning as crucial diagnostic modalities. Aggressive subtypes such as serous, mixed cell, clear cell, and grade 3 endometrioid carcinomas present considerable diagnostic and therapeutic obstacles given their unfavorable prognosis, underscoring the importance of accurate preoperative evaluation. Methods: A retrospective analysis was conducted using data from seventy patients diagnosed with serous, mixed cell, clear cell, or grade 3 endometrioid endometrial cancer, who received surgical treatment from 2020 to 2023. To assess the diagnostic capabilities of each modality in determining final pathology and disease staging, a comparison was performed using results from preoperative endometrial biopsy, MRI, PET/CT, and postoperative histopathology. Cohen's kappa coefficient was employed to determine the level of agreement observed between pre- and postoperative results. Results: Endometrial biopsy demonstrated moderate yet statistically significant concordance with definitive histopathological diagnoses (κ = 0.537, p < 0.001); however, diagnostic errors were observed, especially in instances of mixed and clear cell carcinomas. MRI demonstrated efficacy in identifying local tumor invasion, yet its capacity to detect distant metastases was demonstrably limited. PET/CT was most effective in identifying distant metastases and omental involvement in advanced-stage disease. Conclusions: Definitive pathological diagnosis and staging of endometrial carcinoma are effectively established using endometrial biopsy and MRI. The utility of PET/CT is particularly pronounced in identifying distant metastases in patients with serous carcinoma and advanced-stage disease. Integrating biopsy, MRI, and PET/CT into a multimodal diagnostic strategy enhances diagnostic accuracy and enables personalized treatment planning, particularly for aggressive tumor subtypes.

MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma

Purpose

To evaluate the effectiveness of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics models for predicting deep myometrial invasion (DMI) of early-stage endometrioid adenocarcinoma (EAC).

Methods

The data of 459 EAC patients from three centers were retrospectively collected. Radiomics features were extracted separately from the intratumoral and peritumoral regions expanded by 0 mm, 5 mm, and 10 mm on unimodal and multimodal MRI. Then, various radiomics models were developed and validated, and the optimal model was confirmed. Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. The models' performance was evaluated using the area under the curve (AUC).

Results

The multimodal MRI-based radiomics model, which included both intratumoral and peritumoral regions expanded by 5 mm, was the optimal radiomics model, with an AUC of 0.74 in the validation group. When the same integrated algorithm was utilized, the integrated models with 5-mm expansion presented higher AUCs than those with 0-mm and 10-mm expansion in the validation group. The performance of the stacking model and ensemble model with 5-mm expansion was similar, and their AUCs were 0.74 and 0.75, respectively.

Conclusion

The multimodal radiomics model from the intratumoral and peritumoral regions expanded by 5 mm has the potential to improve the performance for detecting DMI of early-stage EAC. The integrated models are of little value in increasing the prediction.

"The dilemma of GTN versus benign causes of secondary PPH that were indeterminate by ultrasound examination: How to differentiate?: A new prospective diagnostic criterion of functional MRI and ultrasound"

OBJECTIVES

Early differentiation between causes of secondary postpartum hemorrhage (PPH) can sometimes be difficult and can cause serious complications if diagnosis and treatment are delayed. The study aimed to assess the efficacy of different imaging diagnostic criteria, which would help in differentiating between gestational trophoblastic neoplasia (GTN) from indeterminate benign causes; thus, aiding in making clinical decisions in a timely fashion.

METHODS

A comparative prospective study, were 33 patients complaining of 2ry PPH, with indeterminate diagnosis referred to the Radiology department female imaging unit between October 2020 and November 2022 for further assessment. Transvaginal ultrasound examination as well as functional MRI was done. The lesions were characterized and certain diagnostic criteria were evaluated.

RESULTS

The lesion epicenter, margin and depth of myometrial invasion detected by dynamic MRI have significant role to differentiate between GTN and other benign conditions mainly RPOC with higher sensitivity, specificity and accuracy of MRI compared to US. The p value, sensitivity and specificity as well as the accuracy of MRI were: 0.006, 50 %, 92 %, and 81.8 %; 0.000, 87.5 %, 92 % and 90.9 %; 0.002, 87.5 %, 92 % and 90.9 % compared to 0.5, 50 %, 64 % and 60.6 %; 0.01, 87.5 %, 64 % and 69.7 %; 0.2, 87.5 %, 40 % and 51.5 % by US respectively. The overall performance of MRI to reach accurate final diagnosis is higher than the US with accuracy rate of 97 % compared to 63.6 % in indeterminate cases.

CONCLUSIONS

MRI was found to show higher performance, compared to US in differentiating inconclusive cases of 2ry PPH.

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.

Prediction of Lymph Node Metastasis in Endometrial Cancer Based on Color Doppler Ultrasound Radiomics

RATIONALE AND OBJECTIVES

To construct a model using radiomics features based on ultrasound images and evaluate the feasibility of noninvasive assessment of lymph node status in endometrial cancer (EC) patients.

METHODS

In this multicenter retrospective study, a total of 186 EC patients who underwent hysterectomy and lymph node dissection were included, Pathology confirmed the presence or absence of lymph node metastasis (LNM). The study encompassed patients from seven centers, spanning from September 2018 to November 2023, with 93 patients in each group (with or without LNM). Extracted ultrasound radiomics features from transvaginal ultrasound images, used five machine learning (ML) algorithms to establish US radiomics models, screened clinical features through univariate and multivariate logistic regression to establish a clinical model, and combined clinical and radiomics features to establish a nomogram model. The diagnostic ability of the three models for LNM with EC was compared, and the diagnostic performance and accuracy of the three models were evaluated using receiver operating characteristic curve analysis.

RESULTS

Among the five ML models, the XGBoost model performed the best, with AUC values of 0.900 (95% CI, 0.847-0.950) and 0.865 (95% CI, 0.763-0.950) for the training and testing sets, respectively. In the final model, the nomogram based on clinical features and the ultrasound radiomics showed good resolution, with AUC values of 0.919 (95% CI, 0.874-0.964) and 0.884 (0.801-0.967) in the training and testing sets, respectively. The decision curve analysis verified the clinical practicality of the nomogram.

CONCLUSION

The ML model based on ultrasound radiomics has potential value in the noninvasive differential diagnosis of LNM in patients with EC. The nomogram constructed by combining ultrasound radiomics and clinical features can provide clinical doctors with more comprehensive and personalized image information, which is highly important for selecting treatment strategies.

Impact of the 2023 FIGO Staging System for Endometrial Cancer on the Use of Imaging Services: An Indian Perspective

The new 2023 update of the International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer incorporates the advances made in the understanding of the pathology and molecular classifications into the staging system. While the new staging system aids in precision medicine and may lead to better documentation of prognostic risk categories of endometrial cancer, it is complex and calls for an integrated approach and better communication between multiple disciplines involved in the management of endometrial cancer. In this review article, we address how the referral patterns to imaging services might change considering the updated staging system for endometrial cancer. We also discuss the practical aspects and nuances involved in the radiology service delivery and reporting practices as we adopt the new FIGO staging system for managing endometrial cancer patients.

Imaging biomarker for quantitative analysis of endometrial injury based on optical coherence tomography/ultrasound integrated imaging mode

Precise evaluation of endometrial injury is significant to clinical decision-making in gynecological disease and assisted reproductive technology. However, there is a lack of assessment methods for endometrium in vivo. In this research, we intend to develop quantitative imaging markers with optical coherence tomography (OCT)/ultrasound (US) integrated imaging system through intrauterine endoscopic imaging. OCT/US integrated imaging system was established as our previous research reported. The endometrial injury model was established and after treatment, OCT/US integrated imaging and uterus biopsy was performed to evaluate the endometrial thickness, number of superficial fold, and intrauterine area. According to the results, three quantitative indexes acquired from OCT/US image and HE staining have the same trend and have a strong relationship with the severity of the endometrial injury. Accordingly, we developed three imaging markers for quantitative analysis of endometrial injury in vivo, which provided a precise mode for endometrium evaluation in clinical practice.

An MR-based radiomics nomogram including information from the peritumoral region to predict deep myometrial invasion in stage I endometrioid adenocarcinoma: a preliminary study

OBJECTIVE

To develop and validate an MR-based radiomics nomogram combining different imaging sequences (ADC mapping and T2 weighted imaging (T2WI)), different tumor regions (combined intra- and peritumoral regions), and different parameters (clinical features, tumor morphological features, and radiomics features) while considering different MR field strengths in predicting deep myometrial invasion (MI) in Stage I endometrioid adenocarcinoma (EEA).

METHODS

A total of 202 patients were retrospectively analyzed and divided into two cohorts (training cohort, 1.5 T MR, n = 131; validation cohort, 3.0 T MR, n = 71). Axial ADC mapping and T2WI were conducted. Radiomics features were extracted from intra- and peritumoral regions. Least absolute shrinkage and selection operator regression, univariate analysis, and multivariate logistic regression were used to select radiomics features and tumor morphological and clinical parameters. The area under the receiver operator characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and radiomics nomogram.

RESULTS

Ten radiomics features, 4 morphological parameters and 1 clinical characteristic were selected. The radiomics nomogram achieved good discrimination between the superficial and deep MI cohorts. The AUC was 0.927 (95% confidence interval [CI]: 0.865, 0.967) in the training cohort and 0.921 (95% CI: 0.872, 0.948) in the validation cohort. The specificity and sensitivity were 92.0 and 78.9% in the training cohort and 83.0 and 77.8% in the validation cohort, respectively.

CONCLUSION

The radiomics nomogram showed good performance in predicting the depth of MI in Stage I EEA before surgery and might be useful for surgical patient management.

ADVANCES IN KNOWLEDGE

An MR-based radiomics nomogram was useful for predicting deep MI in Stage I EEA patients (AUCtrain = 0.927, AUCvalidation = 0.921). The intra- and peritumoral radiomics features complemented each other. The nomogram was developed and validated with different MR field strengths, suggesting that the model demonstrates good generalizability.

Gynecologic oncology tumor board: the central role of the radiologist

This manuscript is a collaborative, multi-institutional effort by members of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease Focus Panel and the European Society of Urogenital Radiology Women Pelvic Imaging working group. The manuscript reviews the key role radiologists play at tumor board and highlights key imaging findings that guide management decisions in patients with the most common gynecologic malignancies including ovarian cancer, cervical cancer, and endometrial cancer.

Endometrial Adenocarcinoma in Young Women: A Case Report and Review of Literature

Endometrial cancer in young women presents a unique challenge to care teams. With over 90% of cases diagnosed in women over the age of 50, its diagnosis can be delayed in younger patients if the medical team does not maintain a high enough index of suspicion. Once diagnosed, treatment options depend on a desire to maintain fertility. We present a case of a 36-year-old female who, following cross-sectional imaging and pathological analysis, was diagnosed with endometrioid endometrial adenocarcinoma. This case explores the epidemiology of endometrial cancer in young women and the importance of a multi-disciplinary approach to the diagnosis and treatment of this rare malignancy.

Biomedical applications of MnO2 nanomaterials as nanozyme-based theranostics

Manganese dioxide (MnO₂) nanoenzymes/nanozymes (MnO₂-NEs) are 1-100 nm nanomaterials that mimic catalytic, oxidative, peroxidase, and superoxide dismutase activities. The oxidative-like activity of MnO₂-NEs makes them suitable for developing effective and low-cost colorimetric detection assays of biomolecules. Interestingly, MnO₂-NEs also demonstrate scavenging properties against reactive oxygen species (ROS) in various pathological conditions. In addition, due to the decomposition of MnO₂-NEs in the tumor microenvironment (TME) and the production of Mn²⁺, they can act as a contrast agent for improving clinical imaging diagnostics. MnO₂-NEs also can use as an in situ oxygen production system in TME, thereby overcoming hypoxic conditions and their consequences in the progression of cancer. Furthermore, MnO₂-NEs as a shell and coating make the nanosystems smart and, therefore, in combination with other nanomaterials, the MnO₂-NEs can be used as an intelligent nanocarrier for delivering drugs, photosensitizers, and sonosensitizers in vivo. Moreover, these capabilities make MnO₂-NEs a promising candidate for the detection and treatment of different human diseases such as cancer, metabolic, infectious, and inflammatory pathological conditions. MnO₂-NEs also have ROS-scavenging and anti-bacterial properties against Gram-positive and Gram-negative bacterial strains, which make them suitable for wound healing applications. Given the importance of nanomaterials and their potential applications in biomedicine, this review aimed to discuss the biochemical properties and the theranostic roles of MnO₂-NEs and recent advances in their use in colorimetric detection assays of biomolecules, diagnostic imaging, drug delivery, and combinatorial therapy applications. Finally, the challenges of MnO₂-NEs applications in biomedicine will be discussed.

Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators

Aim: To develop and validate a radiomics-based combined model (Model^RC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, Model^RC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: Model^RC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.

Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma

RATIONALE AND OBJECTIVES

To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC).

MATERIALS AND METHODS

A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI).

RESULTS

Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength.

CONCLUSION

The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.

Magnetic Resonance Imaging-Guided Stereotactic Body Radiation Therapy for Medically Inoperable Endometrial Cancer

Endometrial carcinoma is the most frequently diagnosed gynecological cancer among women aged 50 and older in developed countries. In patients who are not amenable to surgery, radiotherapy results in improved survival with acceptable adverse effect profiles. Definitive stereotactic body radiotherapy (SBRT) as a monotherapy remains an unaddressed concept in the literature. Here, we present the case of an 86-year-old woman who was diagnosed with early-stage endometrial carcinoma and was medically inoperable due to cardiac comorbidities. She was treated with magnetic resonance imaging-guided online adaptive radiotherapy-based SBRT. She tolerated the treatment well, with mild increased vaginal discharge. Complete metabolic and radiological responses were obtained. She continues to be disease free in the first year of treatment with no long-term side effects. Our protocol presents promising results with a safe toxicity profile for inoperable early-stage endometrial cancer. Future studies are warranted in light of the current knowledge.

Challenges in Treatment and the Importance of Radiotherapy in a Synchronous Endometrial and Breast Cancer

We report the case of a 69-year-old-female with synchronous endometrial and breast cancer. The imaging and pathology results confirmed the diagnosis. The patient underwent surgery and chemotherapy for endometrial cancer, and surgery followed by external beam radiotherapy for breast cancer. The patient’s clinical condition and imaging showed a favorable evolution after 2 months of follow-up.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis

BACKGROUND

Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date.

PURPOSE

To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI).

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3.

FIELD STRENGTH/SEQUENCE

3-T, T2WI, and T1CE sequences, turbo spin echo.

ASSESSMENT

Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI.

STATISTICAL TESTS

The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834).

DATA CONCLUSION

MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Update on MRI in Evaluation and Treatment of Endometrial Cancer

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

A comparative analysis of MRI findings in endometrial cancer: differentiation between endometrioid adenocarcinoma, serous carcinoma, and clear cell carcinoma

OBJECTIVE

To assess the magnetic resonance imaging (MRI) findings of endometrial cancers and to reveal the differences between endometrioid carcinoma (EC), serous carcinoma (SC), and clear cell carcinoma (CCC).

METHODS

In this study, 274 consecutive patients with histopathologically confirmed endometrial cancer (231 ECs, 25 SCs, and 18 CCCs) who underwent MRI before hysterectomy were enrolled. MRI images were retrospectively reviewed and compared between the three pathologies.

RESULTS

The maximum diameters (55.6 ± 34.7 vs. 39.3 ± 21.6 vs. 39.4 ± 26.8 mm) (p < 0.05) and apparent diffusion coefficient (ADC) values (1.11 ± 0.21 vs. 0.84 ± 0.17 vs. 0.86 ± 0.16 × 10-3 mm2/s) (p < 0.01) were significantly greater in CCCs than in ECs and SCs, respectively. Infiltrative growth pattern (33% vs. 6%) (p < 0.01) was more frequent in CCCs than in ECs. Peritoneal dissemination (16% vs. 0%) (p < 0.01) and heterogeneous signal on diffusion-weighted (61% vs. 32%) (p < 0.05) images were more frequent in SCs than in ECs, respectively. Abnormal ascites (12% vs. 11% vs. 0%) and heterogeneous signal on T1-weighted (28% vs. 50% vs. 9%), T2-weighted (64% vs. 72% vs. 36%), and fat-suppressed gadolinium-enhanced T1-weighted (80% vs. 90% vs. 46%) images were more frequent in SCs and CCCs than in ECs, respectively (p < 0.05).

CONCLUSIONS

SCs frequently exhibited a heterogeneous signal with peritoneal dissemination and abnormal ascites. Alternatively, CCCs tended to have a larger tumor size and higher ADC values with an infiltrative growth pattern, heterogeneous signal, and abnormal ascites.

KEY POINTS

• SCs tend to have a heterogeneous signal intensity with peritoneal dissemination and abnormal ascites compared to ECs. • CCCs tend to have a heterogeneous signal intensity with an infiltrative growth pattern and abnormal ascites compared to ECs. • CCCs have a larger tumor size and higher ADC values compared to ECs and SCs.

Radiomics Nomogram in Assisting Lymphadenectomy Decisions by Predicting Lymph Node Metastasis in Early-Stage Endometrial Cancer

Background

Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients.

Methods

A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated.

Results

The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram.

Conclusions

The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients.

Radiology-pathology correlation of endometrial carcinoma assessment on magnetic resonance imaging

Endometrial carcinoma is the most common gynaecological cancer in developed countries. Most cases are low-volume/low-grade tumour at presentation; however, high-grade subtypes may present with locally advanced disease with higher propensity for spread outside of the pelvis. MRI has a role in local staging of the tumour and helping the clinicians in treatment decision making. This pictorial essay gives examples of endometrial carcinoma at different stages with histological correlation. It also explores the potential limitations and pitfalls of imaging in this context.

Evaluation and Monitoring of Endometrial Cancer Based on Magnetic Resonance Imaging Features of Deep Learning

This study was aimed to compare and analyze the magnetic resonance imaging (MRI) manifestations and surgical pathological results of endometrial cancer (EC) and to explore the clinical research of MRI in the diagnosis and staging of EC. Methods. 80 patients with EC admitted to the hospital were selected as the research objects. The ResNet network was used to optimize the network. When the depth was added, the accuracy of the model was improved, the network parameters were iteratively updated, and the damage function of the minimized network was obtained. The recognition efficiency of MRI images was analyzed using three network modes: shallow CNN network, Res-Net network, and optimized network. The images of EC patients were analyzed, and a quantitative and timed MRI was achieved using simulated datasets in deep learning neural networks, which provided the basis for the formulation of single-scan MRI parameters. All patients underwent preoperative MRI examination using coronal and sagittal T1WI and T2WI imaging. The results showed that the accuracy and specificity of T2 weighted imaging and enhanced scanning in MRI were 88.75% and 95%, respectively. Sensitivity was 87.5%, negative predictive value was 93.75%, and positive predictive value was 86.25%. By MRI examination, 80 cases of EC in patients with stage I diagnosis were 72 cases, accounting for 90%, with endometrial thickening and uneven enhancement. In conclusion, the MRI manifestations of EC are diversified, and MRI has a high value for the staging of EC. MRI examination is conducive to improving diagnostic accuracy.

Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer

Background

Relapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse.

Purpose

To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC.

Methods

For this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (ModelN) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA).

Results

The ModelN demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (ModelC), the discrimination ability of ModelN showed an improvement with P &lt; 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (ModelR), ModelN discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer–Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively).

Conclusion

This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.

Hybrid PET/MRI in Staging Endometrial Cancer: Diagnostic and Predictive Value in a Prospective Cohort

AIM

The assessment of deep myometrial invasion (MI) and lymph node involvement is of utmost importance in the preoperative staging of endometrial cancer (EC). Imaging parameters derived respectively from MRI and PET have shown good predictive value. The main aim of the present study is to assess the diagnostic performance of hybrid 18F-FDG PET/MRI in EC staging, with particular focus on MI and lymphnodal involvement detection.

PATIENTS AND METHODS

Prospective monocentric study including 35 patients with biopsy-proven EC undergoing preoperative 18F-FDG PET/MRI (December 2018-March 2021) for staging purpose. Histological examination was the reference standard. PET (SUVmax, SUVmean with a threshold of 40% of SUVmax-SUVmean40, metabolic tumor volume, total lesion glycolysis) and MRI (volume index [VI], total tumor volume, tumor volume ratio [TVR], mean apparent diffusion coefficient, minimum apparent diffusion coefficient) parameters were calculated on the primary tumor, and their role in predicting EC risk group, the presence of lymphovascular space invasion (LVSI), and MI was assessed. Receiver operating characteristics analysis was used to assess the predictive value of PET and MRI parameters on EC characteristics.

RESULTS

Patients' median age was 66.57 years (SD, 10.21 years). 18F-FDG PET/MRI identified the primary tumor in all patients. Twenty-two of 35 patients had high-risk EC and 13/35 low-risk disease; 13/35 presented LVSI, 22/35 had deep MI at histological examination, and 13/35 had p53 hyperexpression.PET/MRI was able to detect lymphnodal involvement with high accuracy and high specificity (sensitivity of 0.8571, specificity of 0.9286, accuracy of 0.9143), also showing a high negative predictive value (NPV) for lymphnodal involvement (NPV of 0.9630, positive predictive value [PPV] of 0.7500).The assessment of deep MI using PET/MRI correctly staged 27 patients (77.1%; sensitivity of 0.7273, specificity of 0.8462, accuracy of 0.7714), with also a good PPV (PPV of 0.8889, NPV of 0.647).MRI-derived total tumor volume, VI, and TVR were significant in predicting EC groups (high-risk vs low-risk patients) (P = 0.0059, 0.0235, 0.0181, respectively). MRI-derived volume, VI, TVR, and PET-derived metabolic tumor volume and total lesion glycolysis were able to predict LVSI (P = 0.0023, 0.0068, 0.0068, 0.0027, 0.01394, respectively). Imaging was not able to predict grading, presence of deep MI, nor hyperexpression of p53.

CONCLUSIONS

18F-FDG PET/MRI has good accuracy in preoperative staging of EC; PET and MRI parameters have synergic role in preoperatively predicting LVSI, with MRI parameters being also predictive for EC risk group.

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

Staging of Endometrial Cancer Using Fusion T2-Weighted Images with Diffusion-Weighted Images: A Way to Avoid Gadolinium?

Simple Summary

This work aims to evaluate the utility of fusing T2-weighted images with diffusion-weighted images to determinate the depth of myometrial invasion and the stage of endometrial cancer. By showing its superior diagnostic performance, we aim to encourage its use in endometrial cancer staging, and, in the future, obviate the need for intravenous contrast medium administration.

Abstract

Endometrial cancer is the eighth most common cancer worldwide, and its prognosis depends on various factors, with myometrial invasion having a major impact on prognosis. Optimizing MRI protocols is essential, and it would be useful to improve the diagnostic accuracy without the need for other sequences. We conducted a retrospective, single-center study, which included a total of 87 patients with surgically confirmed primary endometrial cancer, and who had undergone a pre-operative pelvic MRI. All exams were read by an experienced radiologist dedicated to urogenital radiology, and the depth of myometrial invasion was evaluated using T2-Weighted Images (T2WI) and fused T2WI with Diffusion-Weighted Images (DWI). Both results were compared to histopathological evaluations. When comparing both sets of imaging (T2WI and fused T2WI-DWI images) in diagnosing myometrial invasion, the fused images had better accuracy, and this difference was statistically significant (p < 0.001). T2WI analysis correctly diagnosed 82.1% (70.6–88.7) of cases, compared to 92.1% correctly diagnosed cases with fused images (79.5–97.2). The addition of fused images to a standard MRI protocol improves the diagnostic accuracy of myometrial invasion depth, encouraging its use, since it does not require more acquisition time.

Role of Imaging in the Management of High-Risk Endometrial Cancer

Endometrial cancer (EC) is the second most frequent gynecological malignancy worldwide with an overall favorable prognosis. However, there is a subgroup of patients with a higher rate of recurrence and worse prognosis who benefit from a specific pre- and post-treatment radiological evaluation that allows the adjustment of the therapeutic attitude towards the biology of the tumor.

The main factors that determine high-risk disease are non-endometrioid tumor histology, histopathological grade 3, lymphovascular space invasion (LVSI), myometrial invasion ≥50%, and cervical stroma involvement. Radiological evaluation helps identify high-risk cases prior to surgical staging and is an important tool both in pre-treatment and in case of clinical recurrence suspicion.

As for imaging techniques, both transvaginal ultrasound and MRI can assess local tumor extent while CT and positron emission tomography/CT (PET/CT) help assess lymph nodes and distant metastases.

The central purpose of this article is to review the specific factors that determine high-risk endometrial cancer, and the main specificities in the pre-treatment and follow-up evaluation according to the most recent international guidelines.

Uterine leiomyomas revisited with review of literature

Uterine leiomyomas, more commonly known as fibroids, are the most common neoplasms of the uterus. These tumors have a profound effect on health care and cost worldwide. Depending on the race, uterine leiomyomas can be seen in 70-80% of all women. Although majority of the women with uterine leiomyomas remain asymptomatic, approximately 30% can present with symptoms. Diagnosing typical leiomyomas on imaging is straightforward. However, when large, located extrauterine and especially with degeneration, the diagnosis can be challenging on imaging. In this article, apart from reviewing the demographics and management of patients with leiomyomas, we describe in detail the imaging appearance of various atypical leiomyomas, uncommon locations outside the uterus and their important differential diagnosis that can have a profound effect on patient management.

PET Imaging for Gynecologic Malignancies

This review article summarizes the clinical applications of established and emerging PET tracers in the evaluation of the 5 most common gynecologic malignancies: endometrial, ovarian, cervical, vaginal, and vulvar cancers. Emphasis is given to 2-deoxy-2-[¹⁸F]fluoro-d-glucose as the most widely used and studied tracer, with additional clinical tracers also explored. The common imaging protocols are discussed, including standard dose ranges and uptake times, established roles, as well as the challenges and future directions of these imaging techniques. The key points are emphasized with images from selected cases.

MRI of the endometrium - from normal appearances to rare pathology

MRI was recently included as a standard pre-operative diagnostic tool for patients with endometrial cancer. MR findings allow a better risk assessment and ultimately guides the surgical planning. Therefore, it is vital that the radiological interpretation is as accurate as possible. This requires essential knowledge regarding the appropriate MRI protocol, as well as different appearances of the endometrium, ranging from normal peri- and post-menopausal changes, benign findings (e.g. endometrial hyperplasia, polyp, changes due to exogenous hormones) to common and rare endometrium-related malignancies. Furthermore, this review will emphasize the role of MRI in staging endometrial cancer patients and highlight pitfalls that could result in the underestimation or overestimation of the disease extent.

The potential value of dequalinium chloride in the treatment of cancer: Focus on malignant glioma

Dequalinium chloride has been known as one kind of antibiotic that displays a broad antimicrobial spectrum and has been clinically proven to be very safe. In recent years, studies have shown that dequalinium chloride can inhibit the growth of malignant tumours, and reports were mainly used for solid tumours. Glioblastoma is the most common malignant neuroepithelial tumour of the central nervous system in adults, and the prognosis of glioblastoma is poor as it has a high resistance to apoptosis. This review summarizes the current understanding of dequalinium chloride-induced cancer cell apoptosis and its potential role in glioblastoma resistance and progression. Particularly, we focus on dequalinium chloride as it exerts a wide range of anti-cancer activity through its ability to target and accumulate in the mitochondria, and it effectively inhibits the growth of glioblastoma cells in vitro and vivo. Dequalinium chloride is an inhibitor of XIAP and can also act as a mitochondrial targeting agent, which gives it an interesting perspective regarding recent advances in the treatment of malignant glioma.

The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer

BACKGROUND

Diffusion-weighted magnetic resonance imaging (DW-MRI) with apparent diffusion coefficient (ADC) measurement provides additional information about tumor microstructure with potential relevance for staging and predicting aggressive disease in patients with endometrial cancer (EC).

PURPOSE

To determine whether ADC values in EC diverge according to the tumor's histologic grade and myometrial invasion depth.

MATERIAL AND METHODS

A sample of 48 pathologically confirmed cases of EC were reviewed retrospectively. The sample was distributed as follows: G1 (n = 9); G2 (n = 18); G3 (n = 21); with myometrial invasion <50% (n = 31); and with myometrial invasion ≥50% (n = 17). DW images were performed at 3.0T with b factors of 0-1000/mm². The region of interest (ROI) was defined within the tumor with T1-weighted and T2-weighted imaging and copied manually to an ADC map. The tumor's grade and myometrial invasion's depth were determined by postoperative histopathological tests.

RESULTS

The means of ADC_min and ADC_mean values were significantly lower for patients with G2 and G3 endometrial tumors than G1. The same tendency was observed in myometrial invasion, as both ADC_min and ADC_mean values were lower for patients with deep than for those with superficial myometrial invasion. The cut-off values of the ADC_min and ADC_mean that predicted high-grade tumors were 0.69 × 10^-3 mm²/s and 0.82 × 10^-3 mm²/s, respectively, while those for myometrial infiltration were 0.70 × 10^-3 mm²/s (ADC_min) and 0.88 × 10^-3 mm²/s (ADC_mean).

CONCLUSION

ADC_min and ADC_mean values correlated with histologic tumor grade and myometrial invasion depth; therefore, it is suggested that ADC on MRI may be a useful indicator to predict malignancy of ECs.

Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.

A Slug-dependent mechanism is responsible for tumor suppression of p53-stabilizing compound CP-31398 in p53-mutated endometrial carcinoma

Mutation in the tumor suppressor gene p53 is the most frequent molecular defect in endometrial carcinoma (EC). Recently, CP-31398, a p53-stabilizing compound, has been indicated to possess the ability to alter the expression of non-p53 target genes in addition to p53 downstream genes in tumor cells. Herein, we explore the alternative mechanisms underlying the restoration of EC tumor suppressor function in mutant p53 by CP-31398. A p53-mutated EC cell was constructed in AN3CA cells with restored or partial loss of Slug using lentiviral vectors, followed by treatment with 25 μM CP-31398. A p53-independent mechanism of CP-31398 was confirmed by the interaction between mouse double minute 2 homolog (MDM2) and Slug AN3CA cells treated with IWR-1 (inhibitor of Wnt response 1). Furthermore, the AN3CA cells were treated with short hairpin RNA against Slug, Wnt-specific activators (LiCl) or inhibitors (XAV-939) followed by CP-31398 treatment. Moreover, AN3CA cell proliferation and apoptosis were examined. A tumorigenicity assay was conducted in nude mice. CP-31398 could promote the apoptosis of p53-mutated EC cells, while Slug reversed this effect. Slug ubiquitination was found to occur via binding of Slug to MDM2 in AN3CA cells. We found that CP-31398 increased the GSK-3ß, p-Slug, Puma, Wtp53, and Bax expressions whereas Wnt, Mtp-53, Slug, Bcl-2, and Ki-67 expressions were decreased. However, these findings were reversed following the activation of the Wnt pathway and overexpression of Slug. Finally, the in vivo experimental evidence confirmed that CP-31398 with depleted Slug suppressed tumor growth by downregulating the Slug. Collectively, CP-31398-regulated Slug downregulation represses the p53-mutated EC via the p53/Wnt/Puma pathway.

Epithelial ovarian cancer: a review of preoperative imaging features indicating suboptimal surgery

Epithelial ovarian cancer has been traditionally treated with cytoreductive surgery and chemotherapy. Optimal surgery is the key to improving the prognosis, and, thus, preoperative imaging should be carefully assessed to determine if the involvement of gastrointestinal, vascular, or thoracic surgeons is necessary to achieve this. Consequently, gynecologists should be able to recognize which imaging features suggest optimal or suboptimal resection. The aim of this review was to present the preoperative imaging features indicating suboptimal resection of epithelial ovarian cancer.

Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer

Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0-0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs). Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results. Results: All of the four models showed predictive ability in LNM. One of the combined models, ModelCR1, consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834-0.951] and test cohort (AUC, 0.883; 95% CI, 0.786-0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3-0.8 cm group, accuracy = 0.846; <0.3 cm group, accuracy = 0.849). Furthermore, compared with the routinely preoperative MR report, the sensitivity and accuracy of this model had a great improvement. Conclusions: A predictive model was proposed based on MR radiomic features and clinical parameters for LNM in EC. The model had a good discrimination ability, especially for normal-sized LNs.

The added value of diffusion-weighted imaging in the preoperative assessment of endometrial cancer

Objective

To evaluate the added value of diffusion-weighted imaging (DWI) in the preoperative assessment of myometrial invasion in endometrial cancer, in comparison with T2-weighted imaging (T2WI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Materials and Methods

This was a retrospective study involving 44 women with endometrial cancer who underwent preoperative 1.5 T MRI. Two radiologists, both of whom were blinded to the histopathology reports, performed a consensus interpretation of the depth of myometrial invasion and of the stage of the cancer, considering three sets of sequences: T2WI, DCE-MRI+T2WI, and DWI+T2WI. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each set. The accuracy was compared with p-value adjustment by the Benjamini-Hochberg procedure.

Results

Among the 44 patients evaluated, DWI+T2WI demonstrated better diagnostic performance in assessing deep myometrial invasion and correctly staged more patients (n = 41) than did DCE-MRI+T2WI (n = 34) and T2WI (n = 22). The superior diagnostic accuracy of DWI+T2WI was statistically significant in comparison with T2WI (p < 0.05) but not in comparison with DCE-MRI+T2WI (p > 0.05).

Conclusion

The addition of DWI apparently improves the diagnostic accuracy of MRI in the preoperative assessment of the depth of myometrial invasion in endometrial cancer, which may be particularly helpful in patients for whom contrast agents are contraindicated.

Role of MRI in staging and follow-up of endometrial and cervical cancer: pitfalls and mimickers

MRI plays important roles in endometrial and cervical cancer assessment, from detection to recurrent disease evaluation. Endometrial cancer (EC) is the most common malignant tumor of the female genital tract in Western countries. EC patients are divided into risk categories based on histopathological tumor type, grade, and myometrial invasion depth. EC is surgically staged using the International Federation of Gynecology and Obstetrics (FIGO) system. Since FIGO (2009) stage correlates with prognosis, preoperative staging is essential for tailored treatment. MRI reveals myometrial invasion depth, which correlates with tumor grade and lymph node metastases, and thus correlates with prognosis. Cervical cancer (CC) is the second most common cancer, and the third leading cause of cancer-related death among females in developing countries. The FIGO Gynecologic Oncology Committee recently revised its CC staging guidelines, allowing staging based on imaging and pathological findings when available. The revised FIGO (2018) staging includes node involvement and thus enables both therapy selection and evaluation, prognosis estimation, and calculation of end results. MRI can accurately assess prognostic indicators, e.g., tumor size, parametrial invasion, pelvic sidewall, and lymph node invasion. Despite these important roles of MRI, radiologists still face challenges due to the technical and interpretation pitfalls of MRI during all phases of endometrial and cervical cancer evaluation. Awareness of mimics that can simulate both cancers is critical. With careful application, functional MRI with DWI and DCE sequences can help establish a correct diagnosis, although it is sometimes necessary to perform biopsy and histopathological analysis.

The ability of ADC measurements in the assessment of patients with stage I endometrial carcinoma based on three risk categories

BACKGROUND

Better selection of patients with intermediate and high-risk stage I endometrial carcinoma (EC) for lymphadenectomy has an important effect on the prognosis.

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) measurements in the assessment of stage I EC patients based on three risk categories.

MATERIAL AND METHODS

We retrospectively studied 80 patients with EC and 28 cervical cancer patients with normal endometrium. 1.5-T conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) (b = 0, 1000 s/mm²) were performed, and ADC values were calculated. Sixty-eight stage I EC patients were divided into three groups: low-risk EC (group 1); intermediate-risk EC (group 2); and high-risk EC (group 3). The remaining 12 EC patients were in stages II and III. Intraclass coefficient, Mann-Whitney U test, Kruskal-Wallis test, and receiver operating characteristics were used for statistical analysis.

RESULTS

The mean ADC values ( × 10^-3 mm² /s) were 0.851 ± 0.131, 0.734 ± 0.108, and 0.710 ± 0.108 for groups 1, 2 and 3, respectively. Significant statistical differences were achieved for the three groups ( P = 0.0005). The mean ADC values of group 1 were significantly lower than those in group 2 + 3 (0.725 ± 0.106; P = 0.0001). For the prediction of groups 2 + 3, the area under the curve of 0.786 and the cut-off value of ≤ 0.742 were identified, with a sensitivity, specificity, and accuracy of 66.67%, 84.09%, and 73.53%, respectively.

CONCLUSION

ADC measurements may have the potential to select intermediate-risk and high-risk stage I EC patients for lymphadenectomy.

Inchworm sign of endometrial cancer on diffusion-weighted MRI: radiology-pathology correlation

AIM

To perform radiology-pathology correlation of the inchworm sign on diffusion-weighted imaging (DWI) in patients with endometrial cancer.

MATERIALS AND METHODS

Consecutive patients (345) with histopathologically proven endometrial cancer who underwent preoperative magnetic resonance imaging (MRI), including DWI images, and hysterectomy were included in the present study. The inchworm sign was defined as a hypointense stalk between hyperintense endometrial cancer and hypointense myometrium on DWI images. A genitourinary pathologist reviewed the resected specimen at the site of the inchworm sign.

RESULTS

The inchworm sign on DWI images was observed in 32 (9.3%) patients. On T2-weighted images, areas of hypointense stalk on DWI images showed hypointensity in 31 (97%) patients and hyperintensity in one (3%). Among them, the depth of myometrial invasion at histopathology was superficial (<50% myometrial invasion) in 28 (87.5%) patients and deep (≥50% myometrial invasion) in four (12.5%). As a result of histopathological investigation, the hypointense stalk of the inchworm sign was mainly composed of various degrees of stromal proliferation, including smooth muscle cells and metaplastic fibromuscular stroma, with or without intervening endometrial cancer.

CONCLUSION

The inchworm sign of endometrial cancer on DWI images usually indicated superficial myometrial invasion and was caused by a stalk composed of stromal proliferation with or without intervening endometrial cancer.