Endometrial Cancer MRI staging: Updated Guidelines of the European Society of Urogenital Radiology

Imaging in pelvic exenteration-a multidisciplinary practice guide from the ESGAR-SAR-ESUR-PelvEx collaborative group

Pelvic exenteration (PE) is a radical surgical approach designed for the curative treatment of advanced pelvic malignancies, requiring en-bloc resection of multiple pelvic organs. While the procedure is radical, it has shown promise in enhancing long-term survival and is now comparable in surgical mortality to elective resections for primary pelvic cancers. Imaging plays a crucial role in preoperative planning, with MRI, CT, and PET/CT being pivotal in assessing the extent of cancer and formulating a surgical roadmap. This paper presents clinical practice guidelines for imaging in the context of PE, developed jointly by ESGAR, SAR, ESUR, and the PelvEx Collaborative. These guidelines aim to standardize imaging protocols and reporting to improve the preoperative assessment and facilitate decision-making in the multidisciplinary treatment of pelvic cancers. Our recommendations underscore the importance of a multidisciplinary approach and the need for clear and precise imaging reports to optimize patient care. CLINICAL RELEVANCE STATEMENT: Our recommendations underscore the importance of a multidisciplinary approach and the need for clear and precise imaging reports to optimize patient care. KEY POINTS: MRI is mandatory for local staging in pelvic exenteration. Structured reporting (using the template provided in this guide) is recommended. Multidisciplinary review of imaging is critical for surgical planning.

Evaluating the image quality and local tumor invasion of uterine cancer by MUSE DWI with RPG

Diffusion-weighted imaging (DWI) is widely utilized for evaluating uterine diseases. However, the prevalent technique, single-shot echo planar imaging (ssEPI), is hindered by notable image distortion and low spatial resolution. Therefore, optimizing uterine DWI sequences is vital for improving image quality. To investigate the efficacy of multiplexed sensitivity encoding (MUSE) combined with reverse polarity gradient (RPG) in enhancing uterine DWI quality and assessing local invasion in endometrial and cervical cancer, we included 149 patients. Each patient underwent DWI of the uterus using ssEPI, MUSE, and RPG-MUSE techniques. We compared these three sequences regarding image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion rate (GDR), ADC values, accuracy in determining the extent of cancer invasion, and the Area Under the Curve (AUC) for identifying endometrial cancer and benign endometrial lesions using ADC values. The results indicated that RPG-MUSE DWI had less artifacts than MUSE and ssEPI (P < 0.05). Lesions were more apparent in MUSE and RPG-MUSE sequences compared to ssEPI (P < 0.05), with RPG-MUSE providing clearer lesion edges (P < 0.05). Additionally, RPG-MUSE DWI demonstrated higher SNR and CNR than ssEPI and MUSE (P < 0.05), along with a lower GDR (P < 0.05). The ADC values did not show significant differences among the three sequences (P > 0.05). Furthermore, the AUC of the ROC for detecting endometrial cancer and benign endometrial lesions using ADC values showed no significant differences across the sequences (P = 0.7609, 0.7186, and 0.8706, respectively). When combining each DWI sequence with T2WI for FIGO staging, RPG-MUSE and MUSE exhibited better alignment with pathology findings compared to ssEPI (P < 0.05). Overall, RPG-MUSE DWI showed fewer artifacts, higher SNR and CNR, reduced geometric distortion, and clearer lesion visualization compared to ssEPI and MUSE, leading to a more precise assessment of endometrial and cervical cancer invasion extent.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Study on the value of MRI in locating the internal OS of the cervix and influencing factors

The position of the internal os of the cervix reported in the literature was inconsistent on MRI images. Additionally, the practical impactful data influencing the internal os located by MRI is limited. We aimed to confirm the position of the internal os of the cervix on MRI images, and the influencing factors locating the the internal os by MRI. A single-center retrospective study was conducted. Data from 175 patients who underwent total hysterectomy for stage I endometrial cancer were collected. The internal os of the cervix is positioned as the starting point for measuring the length of the cervix on MRI images. On dynamic contrast-enhanced MRI (DCE-MRI), the section formed by the enhancement difference between the uterus and cervix, and on T2-weighted imaging(T2WI), the section formed by the physiological curvature of the uterus and the low signal intensity of the cervical stroma were used as starting points. The results showed no statistically significant difference compared with the removed uterus specimens (p = 0.208, p = 0.571, p = 0.804). A history of cesarean section(p < 0.001), irregular vaginal bleeding for more than three months(p < 0.001), cervical adenomyosis(p = 0.043), and premenopause(p = 0.001) were not conducive to locating the internal os of the cervix by MRI. Our findings provide valuable information and confirm the position of the internal os of the cervix on MRI images, and the several important infuencing factors. We hope that some patients will benefit from our study.

Training radiology residents to evaluate deep myometrial invasion in endometrial cancer patients on MRI: A learning curve study

PURPOSE

To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI.

METHOD

Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions. Each subset consisted of 30 scans, the first and the last with the same cases, for a total of 150 reads. Diagnostic accuracy metrics, assessment time (rounded to the nearest minute), and confidence levels (using a 5-point Likert scale) were recorded. The learning curve was obtained plotting the diagnostic accuracy of the three trainees and the average over the subsets. Anatomopathological results served as the reference standard for DMI presence.

RESULTS

The three trainees exhibited heterogeneous starting point, with a learning curve and a trend to more homogeneous performance with training. The diagnostic accuracy of the average trainee raised from 64 % (56 %-76 %) to 88 % (80 %-94 %) across the five subsets (p < 0.001). Reductions in assessment time (5.92 to 4.63 min, p < 0.018) and enhanced confidence levels (3.58 to 3.97, p = 0.12) were observed. Improvements in sensitivity, specificity, positive predictive value, and negative predictive value were noted, particularly for specificity which raised from 56 % (41 %-68 %) in the first to 86 % (74 %-94 %) in the fifth subset (p = 0.16). Although not reaching statistical significance, these advancements aligned the trainees with literature performance benchmarks.

CONCLUSIONS

The structured training program significantly enhanced radiology residents' diagnostic accuracy in assessing DMI for EC on MRI, emphasizing the effectiveness of active case-based training in refining oncologic imaging skills within radiology residency curricula.

Stage II endometrial cancer: The diagnostic power of hysteroscopic excisional biopsy and MRI in the pre-operative cervical stroma assessment

INTRODUCTION/BACKGROUND

Stage II Endometrial cancer (EC) accounts only for 12% of cases. Recent evidences redraw the weight of radicality in this stage as it would seem to have no impact on survival outcomes claiming for radicality when free surgical margins are not ensured to be achieved by simple hysterectomy. Thus, an accurate pre-operative evaluation might be crucial. This study aims to estimate the diagnostic power of Hysteroscopic excisional biopsy (HEB) of cervical stroma alone and combined with Magnetic resonance imaging (MRI) to predict the stage and concealed parametrial invasion in patients with preoperative stage II EC.

METHODOLOGY

From January 2019 to November 2023, all patients evaluated at the Department of Gynaecology Oncology of Humanitas, Istituto Clinico Catanese, Catania, Italy, with a diagnosis of EC and evidence of cervical stromal diffusion on preoperative MRI and/or office hysteroscopy evaluation, considered suitable for laparoscopic modified type B hysterectomy, were consecutively included in the study. These underwent endometrial and cervical hysteroscopy excisional biopsy (HEB) for histological evaluation before definitive surgery. The data obtained were compared with the definitive histological examination (reference standard).

RESULTS

Sixteen patients met the including/excluding criteria and were considered into the study. Stage II endometrial cancer were confirmed in 3 cases (18.7%). We reported 2 (12,5%) parametrial involvement (IIIB), 4 (25%) cases of lymph nodes metastasis (IIIc), 7 (43,7%) cases of I stage. MRI had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (95% CIs) of 71%, 44%, 50%, 66% and 56.2 % respectively. HEB showed sensitivity, specificity, PPV, NPV and accuracy (95 % CI) of 85 %, 89 %, 85 %, 88 % and 87 % respectively. Comparing HEB + MRI to HEB alone, no statistical differences were noted in all fields. Considering parametrial invasion, MRI had better sensitivity but there were no statistical differences to HEB in other fields, showing both a worthy NPV.

CONCLUSION

HEB was accurate in all fields for cervical stroma assessment and had a fine NPV to exclude massive cervical involvement up to parametrial. Considering the new FIGO staging a preoperative molecular and histological evaluation of the cervical stroma may be useful. Operative hysteroscopy seems to be a feasible and accurate method for this purpose.

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.

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.

Apparent Diffusion Coefficient on Diffusion-Weighted Magnetic Resonance Imaging to Predict the Prognosis of Patients with Endometrial Cancer: A Meta-Analysis

Apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (DWI) may help diagnose endometrial cancer (EC). However, the association between ADC and the recurrence and survival of EC remains unknown. We performed a systematic review and meta-analysis to investigate whether pretreatment ADC on DWI could predict the prognosis of women with EC. PubMed, Embase, and Cochrane's Library were searched for relevant cohort studies comparing the clinical outcomes between women with EC having low versus high ADC on pretreatment DWI. Two authors independently conducted data collection, literature searching, and statistical analysis. Using a heterogeneity-incorporating random-effects model, we analyzed the results. In the meta-analysis, 1358 women with EC were included from eight cohort studies and followed for a median duration of 40 months. Pooled results showed that a low pretreatment ADC on DWI was associated with poor disease-free survival (DFS, hazard ratio [HR]: 3.29, 95% CI: 2.04 to 5.31, p < 0.001; I2 = 41%). Subgroup analysis according to study design, tumor stage, MRI Tesla strength, ADC cutoff, follow-up duration, and study quality score showed consistent results (p for subgroup analysis all > 0.05). The predictive value of low ADC for poor DFS in women with EC decreased in multivariate studies compared to univariate studies (HR: 2.59 versus 32.57, p = 0.002). Further studies showed that a low ADC was also associated with poor overall survival (HR: 3.36, 95% CI: 1.33 to 8.50, p = 0.01, I2 = 0). In conclusion, a low ADC on pretreatment DWI examination may predict disease recurrence and survival in women with EC.

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).

Nodal infiltration in endometrial cancer: a prediction model using best subset regression

OBJECTIVES

To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors.

METHODS

In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available.

RESULTS

The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient β values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively.

CONCLUSIONS

The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies.

CLINICAL RELEVANCE STATEMENT

The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment.

KEY POINTS

•Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

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

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

AI-Supported Autonomous Uterus Reconstructions: First Application in MRI Using 3D SPACE with Iterative Denoising

RATIONALE AND OBJECTIVES

T2-weighted imaging in at least two orthogonal planes is recommended for assessment of the uterus. To determine whether a convolutional neural network-based algorithm could be used for the re-constructions of uterus axes derived from a 3D SPACE with iterative denoising.

MATERIALS AND METHODS

50 patients aged 18-81 (mean: 42) years who underwent an MRI examination of the uterus participated voluntarily in this prospective study after informed consent. In addition to a standard MRI pelvis protocol, a 3D SPACE research application sequence was acquired in sagittal orientation. Reconstructions for both the cervix and the cavum in the short and long axes were performed by a research trainee (T), an experienced radiologist (E), and the prototype software (P). In the next step, the reconstructions were evaluated anonymously by two experienced readers according to 5-point-Likert-Scales. In addition, the length of the cervical canal, the length of the cavum and the distance between the tube angles were measured on all reconstructions. Interobserver agreement was assessed for all ratings.

RESULTS

For all axes, significant differences were found between the scores of the reconstructions by research T, E and P. P received higher scores and was preferred significantly more often with the exception of the comparison of the reconstruction Cervix short of E (Cervix short: P vs. T: p = 0.02; P vs. E: p = 0.26; Cervix long: P vs. T: p = 0.01; P vs. E: p < 0.01; Cavum short: P vs. T: p = 0.01; P vs. E: p = 0.02; Cavum long: P vs. T: p < 0.01; P vs. E: p < 0.01). Regarding the measured diameters, (length of cervical canal/cavum/distance between tube angles) significantly larger diameters were recorded for P compared to E and T (Cervix long (mm): T: 25.43; E: 25.65; P: 26.65; Cavum short (mm): T: 26.24; E: 25.04; P: 27.33; Cavum long (mm): T: 31.98; E: 32.91; P: 34.41; P vs. T: p < 0.01); P vs. E: p = 0.04). Moderate to substantial agreement was found between Reader 1 and Reader 2 (range: 0.39-0.67).

CONCLUSION

P was able to reconstruct the axes at least as well as or better than E and T. P could thereby lead to workflow facilitation and enable more efficient reporting of uterine MRI.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma

OBJECTIVE

To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA).

METHODS

Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.

RESULTS

A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively.

CONCLUSION

Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

Clinical Utility of Diffusion-Weighted Imaging in Gynecological Imaging: Revisited

ABSTRACT

Diffusion-weighted imaging (DWI) is an increasingly valuable sequence in daily clinical practice, providing both functional and morphological information. The use of DWI can help quantify diffusion using the apparent diffusion coefficient, which reflects the physiological features of the tissue and tumor microcirculation. This knowledge is crucial for understanding and interpreting gynecological imaging. This article reviews the clinical utility of DWI for gynecological imaging, highlighting its ability to aid in the detection of endometrial and cervical cancers, as well as tumor extension and metastasis. In addition, DWI can easily detect the solid components of ovarian cancer (including dissemination), assist in the diagnosis of adnexal torsion, and potentially show bone marrow status. Apparent diffusion coefficient measurement is useful for differentiating between endometrial lesions, uterine leiomyomas, and sarcomas, and may provide important information for predicting the prognosis of gynecological cancers.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

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.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Post-Menopausal Bleeding: Role of Imaging

Post-menopausal bleeding (PMB) accounts for 5% of gynecologic office visits and is the presenting symptom in 90% of women with endometrial cancer, which requires prompt evaluation. The most common etiology of PMB is vaginal or endometrial atrophy and endometrial polyps, while endometrial hyperplasia and carcinoma account for less than 10% of PMB. Transvaginal ultrasonography measurement of an endometrial thickness (EMT) less than or equal to 4 mm has a 99% negative predictive value for endometrial carcinoma. Endometrial sampling is required if EMT >4 mm or persistent bleeding occurs. Further evaluation can be accomplished with saline infusion sonography, magnetic resonance imaging, and hysteroscopy.

Contrast-Enhanced MRI in Women with Endometrial Cancer: Dynamic Versus Single-Phase Acquisitions

Background

The 2019 European Society of Urogenital Radiology (ESUR) guidelines for endometrial cancer recommend performing either dynamic contrast-enhanced magnetic resonance imaging (CE MRI) or single-phase CE MRI. However, no study has directly compared these options. Therefore, this study compared dynamic versus single-phase CE MRI for the evaluation of myometrial invasion in women with endometrial cancer.

Methods

This retrospective, single-institution comparative study was conducted among women with surgically proven endometrial cancer, including 30 consecutive women with single-phase CE MRI and 30 age- and pathologic stage-matched women with dynamic CE MRI. Three readers independently compared dynamic and single-phase CE MRI in terms of the tumor-myometrium signal intensity (SI) difference ratio, depth of myometrial invasion, image quality, and image number. Pathologic findings served as a reference standard for the depth of myometrial invasion.

Results

The estimated mean SI difference ratios of dynamic CE MRI and single-phase CE MRI fell within an equivalence margin of 0.05 (90% confidence intervals [CIs] = [-0.0497 to -0.0165], [-0.0226 to -0.0403], and [-0.0429 to -0.0433], respectively, for readers A, B, and C). The area under the receiver operating characteristic curve for the detection of deep myometrial invasion was not significantly different between the acquisitions (P = .3315, P = .3345, and P = .8593, respectively). Single-phase CE MRI showed significantly better image quality than dynamic CE MRI (P = .0143, P = .0042, and P = .0066, respectively), while the median number of images for dynamic CE MRI was 2.4 times higher than that for single-phase CE MRI.

Conclusion

Single-phase acquisition may be a better option for CE MRI in women with endometrial cancer than dynamic acquisition.

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.

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.

Gynaecological Causes of Acute Pelvic Pain: Common and Not-So-Common Imaging Findings

In female patients, acute pelvic pain can be caused by gynaecological, gastrointestinal, and urinary tract pathologies. Due to the variety of diagnostic possibilities, the correct assessment of these patients may be challenging. The most frequent gynaecological causes of acute pelvic pain in non-pregnant women are pelvic inflammatory disease, ruptured ovarian cysts, ovarian torsion, and degeneration or torsion of uterine leiomyomas. On the other hand, spontaneous abortion, ectopic pregnancy, and placental disorders are the most frequent gynaecological entities to cause acute pelvic pain in pregnant patients. Ultrasound (US) is usually the first-line diagnostic technique because of its sensitivity across most common aetiologies and its lack of radiation exposure. Computed tomography (CT) may be performed if ultrasound findings are equivocal or if a gynaecologic disease is not initially suspected. Magnetic resonance imaging (MRI) is an extremely useful second-line technique for further characterisation after US or CT. This pictorial review aims to review the spectrum of gynaecological entities that may manifest as acute pelvic pain in the emergency department and to describe the imaging findings of these gynaecological conditions obtained with different imaging techniques.

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.

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

BACKGROUND

Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients.

METHODS

Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc).

RESULTS

In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models.

CONCLUSIONS

Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

Field-of-view optimized and constrained undistorted single-shot study of intravoxel incoherent motion and diffusion-weighted imaging of the uterus during the menstrual cycle: a prospective study

PURPOSE

This study aimed to compare the variability of the uterus during the menses phase (MP), follicular phase (FP), and luteal phase (LP) of the menstrual cycle using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

METHODS

This prospective study was conducted at the Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2022 and January 2023. Women of childbearing age (18-45 years) with appropriate progesterone levels were included in this study. Conventional magnetic resonance imaging and IVIM-DWI scans were performed during the MP, FP, and LP. The differences in IVIM-DWI-derived parameters between these phases were then compared, and the overlap was quantitatively described.

RESULTS

The apparent diffusion coefficient (ADC) and pure molecular diffusion coefficient (D) values from the endometrium, uterine junctional zone (UJZ), and myometrium indicated statistical differences between the MP and FP and the MP and LP (ADC: endometrium, both P < 0.001; UJZ, P = 0.008 and P < 0.001, respectively; myometrium, P = 0.033 and P = 0.006, respectively; D: endometrium, both P < 0.001; UJZ, P = 0.008 and P = 0.006, respectively; myometrium, P = 0.041 and P = 0.045, respectively). The perfusion-related diffusion coefficient (D*) values from the myometrium indicated statistical differences between the FP and MP and the FP and LP (D*: myometrium, P = 0.049 and P = 0.009, respectively). The overlapping endometrium ratios between the MP and FP or LP were lower than 50% in the ADC and D values (ADC: overlapping of MP and FP: 33.33%, overlapping of MP and LP: 23.33%; D: overlapping of MP and FP: 40.00%, overlapping of MP and LP: 43.33%).

CONCLUSION

The ADC and IVIM-derived parameters indicated differences in the uterus in diverse phases of the menstrual cycle, especially in the endometrium in relation to ADC and D values.

Dissemination of endometrial cancer MRI staging guidelines among young radiologists: an ESUR Junior Network survey

OBJECTIVES

Imaging guidelines could play an important role in the training of radiologists, but the extent of their adoption in residency programs is unclear. With this survey, the European Society of Urogenital Radiology (ESUR) Junior Network aimed to assess the dissemination of the ESUR guidelines on endometrial cancer MRI staging (EC-ESUR guidelines) among young radiologists.

METHODS

An online questionnaire targeted to last year radiology residents and radiologists in the first year of their career was designed. It included 24 questions, structured in 4 sections (i.e., background, general, acquisition protocol, interpretation, and reporting). The survey was active between April and May 2022, accepting answers worldwide. Answers were solicited with a social media campaign and with the support of national scientific societies. Subgroup analysis was performed based on variables such as subspecialty of interest and number of EC-ESUR guidelines consultations using the Wilcoxon rank sum test.

RESULTS

In total, 118 participants completed the questionnaire, of which 94 (80%) were from Europe and 46 (39%) with a special interest in urogenital radiology. Overall, 68 (58%) stated that the guidelines were not part of their residency teaching programs while 32 (27%) had never even consulted the guidelines. Interest in urogenital radiology as a subspecialty and EC-ESUR guidelines consultations were associated with greater confidence in supervising scan acquisition, interpreting, and reporting EC MRI staging exams.

CONCLUSION

Four years after publication, the adoption of EC-ESUR guidelines in residency programs is heterogeneously low. Despite a possible selection bias, our findings indicate that active promotion of EC-ESUR guidelines is required.

KEY POINTS

• The adoption of ESUR guidelines on endometrial cancer in radiology residency programs is heterogeneous. • Almost one third of respondents stated they had never even consulted the guidelines. • Confidence toward guidelines was higher in those who were exposed to more endometrial cancer MRI staging scans. • Reading the guidelines was associated with a greater confidence in protocol acquisition, interpretation, and reporting. • Active efforts to promote their dissemination are required.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region

BACKGROUND

Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making.

PURPOSE

To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA).

MATERIAL AND METHODS

A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts.

RESULTS

Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively.

CONCLUSION

The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Adenomyosis at a Glance: An Integrated Review of Transvaginal Ultrasound and MR Imaging Findings

Adenomyosis is a benign uterine disorder increasingly recognized in premenopausal women. Given its significant clinical burden, an accurate noninvasive diagnosis is paramount. Both transvaginal ultrasound (TVUS) and magnetic resonance (MR) provide an adequate assessment of adenomyosis, the former being recommended for first-line imaging evaluation and the latter being mainly used as a problem-solving technique. In this article, the authors review the TVUS and MR imaging findings of adenomyosis while referring to their histopathological background. Whereas direct signs correlate directly to ectopic endometrial tissue and are highly specific to adenomyosis, indirect signs result from myometrial hypertrophy and increase diagnostic sensitivity. Potential pitfalls, differential diagnoses, and frequently associated estrogen-dependent conditions are also discussed.

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.

Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Objective

The application value of T2 mapping in evaluating endometrial carcinoma (EMC) features remains unclear. The aim of the study was to determine the quantitative T2 values in EMC using a novel accelerated T2 mapping, and evaluate them for detection, classification,and grading of EMC.

Materials and methods

Fifty-six patients with pathologically confirmed EMC and 17 healthy volunteers were prospectively enrolled in this study. All participants underwent pelvic magnetic resonance imaging, including DWI and accelerated T2 mapping, before treatment. The T2 and apparent diffusion coefficient (ADC) values of different pathologic EMC features were extracted and compared. Receiver operating characteristic (ROC) curve analysis was performed to analyze the diagnostic efficacy of the T2 and ADC values in distinguishing different pathological features of EMC.

Results

The T2 values and ADC values were significantly lower in EMC than in normal endometrium (bothl p < 0.05). The T2 and ADC values were significantly different between endometrioid adenocarcinoma (EA) and non-EA (both p < 0.05) and EMC tumor grades (all p < 0.05) but not for EMC clinical types (both p > 0.05) and depth of myometrial invasion (both p > 0.05). The area under the ROC curve (AUC) was higher for T2 values than for ADC values in predicting grade 3 EA (0.939 vs. 0.764, p = 0.048). When combined T2 and ADC values, the AUC for predicting grade 3 EA showed a significant increase to 0.947 (p = 0.03) compared with those of ADC values. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.706 and r = -0.537, respectively).

Conclusion

Quantitative T2 values demonstrate potential suitability in discriminating between EMC and normal endometrium, EA and non-EA, grade 3 EA and grade 1/2 EA. Combining T2 and ADC values performs better in predicting the histological grades of EA in comparison with ADC values alone.

MR Imaging of Gynecologic Tumors: Pearls, Pitfalls, and Tumor Mimics

MR imaging is the modality of choice for the pre-treatment evaluation of patients with gynecologic malignancies, given its excellent soft tissue contrast and multi-planar capability. However, it is not without pitfalls. Challenges can be encountered in the assessment of the infiltration of myometrium, vagina, cervical stroma, and parametria, which are crucial prognostic factors for endometrial and cervical cancers. Other challenges can be encountered in the distinction between solid and non-solid tissue and in the identification of peritoneal carcinomatosis for the sonographically indeterminate adnexal mass.

Recommandations pour la pratique clinique Nice/Saint-Paul-de-Vence 2022–2023 : prise en charge du cancer de l'endomètre localisé

Recommendations for clinical practice, Nice/Saint-Paul-de-Vence 2022-2023: Management of localized endometrial cancer Endometrial cancer is the most frequent gynecological cancers in industrialized countries and its incidence increases. The newmolecularclassification allows determination of the risk of recurrence and helps orienting therapeutic management. Surgery remains the cornerstone of treatment. Minimally invasive approach must be preferred for stages I and II. Surgery includes hysterectomy with bilateral adnexectomy, sentinel lymph node biopsy even in high risk diseases and omentectomy for non-endometrioid tumors (except in case of clear cells tumors). Fertility preservation can be proposed in low grade, stage I tumors without myometrial involvement. In stage III/IV disease, lymph node debulking without totallymphadenectomy is indicated. In case of peritoneal carcinomatosis, first-line cytoreductive surgery is recommended if complete resection can be achieved. Adjuvant therapy is not recommended in low risk tumors. In intermediate risk tumors, curietherapy is indicated. In tumors with high-intermediate risk, curietherapy and external radiotherapy are indicated according to prognostic factors (stage II, lymphovascular invasion); adjuvant chemotherapy can be considered on a case-by-case basis. In high risk tumors, chemotherapy and external radiotherapy are recommended using a concomitant or sequential approach.

Diagnostic accuracy of MRI for evaluating myometrial invasion in endometrial cancer: a comparison of MUSE-DWI, rFOV-DWI, and DCE-MRI

OBJECTIVES

To compare the image quality of high-resolution diffusion-weighted imaging (DWI) using multiplexed sensitivity encoding (MUSE) versus reduced field-of-view (rFOV) techniques in endometrial cancer (EC) and to compare the diagnostic performance of these techniques with that of dynamic contrast-enhanced (DCE) MRI for assessing myometrial invasion of EC.

METHODS

MUSE-DWI and rFOV-DWI were obtained preoperatively in 58 women with EC. Three radiologists assessed the image quality of MUSE-DWI and rFOV-DWI. For 55 women who underwent DCE-MRI, the same radiologists assessed the superficial and deep myometrial invasion using MUSE-DWI, rFOV-DWI, and DCE-MRI. Qualitative scores were compared using the Wilcoxon signed-rank test. Receiver operating characteristic analysis was performed to compare the diagnostic performance.

RESULTS

Artifacts, sharpness, lesion conspicuity, and overall quality were significantly better with MUSE-DWI than with rFOV-DWI (p < 0.05). The area under the curve (AUC) of MUSE-DWI, rFOV-DWI, and DCE-MRI for the assessment of myometrial invasion were not significantly different except for significantly higher AUC of MUSE-DWI than that of DCE-MRI for superficial myometrial invasion (0.76 for MUSE-DWI and 0.64 for DCE-MRI, p = 0.049) and for deep myometrial invasion (0.92 for MUSE-DWI and 0.80 for DCE-MRI, p = 0.022) in one observer, and that of rFOV-DWI for deep myometrial invasion in another observer (0.96 for MUSE-DWI and 0.89 for rFOV-MRI, p = 0.048).

CONCLUSION

MUSE-DWI exhibits better image quality than rFOV-DWI. MUSE-DWI and rFOV-DWI shows almost equivalent diagnostic performance compared to DCE-MRI for assessing superficial and deep myometrial invasion in EC although MUSE-DWI may be helpful for some radiologists.

Endometrial Cancer Staging: Is There Value in ADC?

PURPOSE

To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement.

METHODS

Patients with endometrial cancers diagnosed between 2018-2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification.

RESULTS

The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery.

CONCLUSIONS

ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

PURPOSE

To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.

METHODS

A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models.

RESULTS

Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively.

CONCLUSION

It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma

PURPOSE

To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC).

METHODS

Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference.

RESULTS

Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively.

CONCLUSION

An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.

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.

Development of MRI-based radiomics predictive model for classifying endometrial lesions

An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.

Imaging Recommendations for Diagnosis, Staging, and Management of Uterine Cancer

Uterine cancers are classified into cancers of the corpus uteri (uterine carcinomas and carcinosarcoma) and corpus uteri (sarcomas) by the AJCC staging system (eighth edition). Endometrial carcinoma is the most common amongst these with prolonged estrogen exposure being a well-known risk factor. The FIGO staging system for endometrial carcinoma is primarily surgical and includes total hysterectomy, bilateral salpingo-oophorectomy, and lymphadenectomy. Imaging is useful in the preoperative evaluation of tumor stage, especially assessment of myometrial invasion and cervical stromal extension. Dynamic contrast enhanced MRI with DWI has a high staging accuracy and is the preferred imaging modality for primary evaluation with contrast-enhanced CT abdomen being indicated for recurrent disease. PET/CT is considered superior in evaluation of lymph nodes and extra pelvic metastases.

High-Sensitivity Mutation Analysis of Cell-Free DNA for Disease Monitoring in Endometrial Cancer

PURPOSE

We sought to determine whether sequencing analysis of circulating cell-free DNA (cfDNA) in patients with prospectively accrued endometrial cancer captures the mutational repertoire of the primary lesion and allows for disease monitoring.

EXPERIMENTAL DESIGN

Peripheral blood was prospectively collected from 44 newly diagnosed patients with endometrial cancer over a 24-month period (i.e., baseline, postsurgery, every 6 months after). DNA from the primary endometrial cancers was subjected to targeted next-generation sequencing (NGS) of 468 cancer-related genes, and cfDNA to a high-depth NGS assay of 129 genes with molecular barcoding. Sequencing data were analyzed using validated bioinformatics methods.

RESULTS

cfDNA levels correlated with surgical stage in endometrial cancers, with higher levels of cfDNA being present in advanced-stage disease. Mutations in cfDNA at baseline were detected preoperatively in 8 of 36 (22%) patients with sequencing data, all of whom were diagnosed with advanced-stage disease, high tumor volume, and/or aggressive histologic type. Of the 38 somatic mutations identified in the primary tumors also present in the cfDNA assay, 35 (92%) and 38 (100%) were detected at baseline and follow-up, respectively. In 6 patients with recurrent disease, changes in circulating tumor DNA (ctDNA) fraction/variant allele fractions in cfDNA during follow-up closely mirrored disease progression and therapy response, with a lead time over clinically detected recurrence in two cases. The presence of ctDNA at baseline (P < 0.001) or postsurgery (P = 0.014) was significantly associated with reduced progression-free survival.

CONCLUSIONS

cfDNA sequencing analysis in patients with endometrial cancer at diagnosis has prognostic value, and serial postsurgery cfDNA analysis enables disease and treatment response monitoring. See related commentary by Grant et al., p. 305.

Accuracy of transvaginal ultrasound versus MRI in the PreOperative Diagnostics of low-grade Endometrial Cancer (PODEC) study: a prospective multicentre study

AIM

To investigate if the diagnostic accuracy of transvaginal ultrasound (TVUS) performed by gynaecologists is sufficient for preoperative assessment of low-grade endometrial cancer (EC) compared to magnetic resonance imaging (MRI).

MATERIALS AND METHODS

MRI and TVUS performed by gynaecologists were assessed at the participating centres. The MRI examinations were interpreted by two radiologists at the tertiary centre. Deep myometrial and cervical stroma invasion were visually assessed and compared to postoperative histopathology.

RESULTS

Two hundred and fifty-nine patients were included. There was a statistically significant difference in specificity assessing deep myometrial invasion between MRI and TVUS (MRI 0.88, TVUS 0.68). There was no difference in sensitivity (MRI 0.73, TVUS 0.68). When assessing cervical stroma infiltration, MRI had a higher specificity (MRI 0.96, TVUS 0.90), but there was no difference in sensitivity (MRI 0.41, TVUS 0.32).

CONCLUSION

MRI has higher specificity than TVUS performed by gynaecologists for assessing deep MI and CSI in low-grade EC, but similar sensitivities. The use of TVUS as a first-line test, rather than MRI, may be supported by this study in centres where access to MRI may be limited.

Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality

OBJECTIVES

To investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2_DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2_S) as reference.

METHODS

In total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2_S and T2_DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences.

RESULTS

T2_DL resulted in a 62.7% timing reduction (1:54 min for T2_DL and 5:06 min for T2_S in axial, sagittal, and coronal imaging, respectively). Compared to T2_S, T2_DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925-0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall's W > 0.75). T2_DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging.

CONCLUSIONS

Compared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.

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

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

Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation

BACKGROUND

Treatment monitoring in metastatic colorectal cancer (mCRC) relies on imaging to evaluate the tumour burden. Response Evaluation Criteria in Solid Tumors provide a framework on reporting and interpretation of imaging findings yet offer no guidance on a standardised imaging protocol tailored to patients with mCRC. Imaging protocol heterogeneity remains a challenge for the reproducibility of conventional imaging end-points and is an obstacle for research on novel imaging end-points.

PATIENTS AND METHODS

Acknowledging the recently highlighted potential of radiomics and artificial intelligence tools as decision support for patient care in mCRC, a multidisciplinary, international and expert panel of imaging specialists was formed to find consensus on mCRC imaging protocols using the Delphi method.

RESULTS

Under the guidance of the European Organisation for Research and Treatment of Cancer (EORTC) Imaging and Gastrointestinal Tract Cancer Groups, the European Society of Oncologic Imaging (ESOI) and the European Society of Gastrointestinal and Abdominal Radiology (ESGAR), the EORTC-ESOI-ESGAR core imaging protocol was identified.

CONCLUSION

This consensus protocol attempts to promote standardisation and to diminish variations in patient preparation, scan acquisition and scan reconstruction. We anticipate that this standardisation will increase reproducibility of radiomics and artificial intelligence studies and serve as a catalyst for future research on imaging end-points. For ongoing and future mCRC trials, we encourage principal investigators to support the dissemination of these imaging standards across recruiting centres.

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.