Assessment of deep myometrial invasion of endometrial cancer on MRI: added value of second-opinion interpretations by radiologists subspecialized in gynaecologic oncology

Is it safe to operate selected low-risk endometrial cancer patients in secondary hospitals?

INTRODUCTION

The aim of this study was to assess the accuracy of a preoperative screening algorithm in identifying low-risk endometrial cancer (EC) patients to ensure optimal care.

METHODS

A total of 277 patients with primary EC confirmed through biopsy underwent magnetic resonance imaging (MRI). Patients with risk factors for advanced high-risk EC, such as non-endometrioid histology, high-grade differentiation status, deep myometrial invasion, or spread beyond the uterine corpus, were systematically excluded. The remaining preoperatively screened patients with stage IA low-grade endometrioid EC (EEC) (n = 93) underwent surgery in a tertiary hospital. The accuracy of the preoperative diagnosis was evaluated by comparing the findings with the postoperative histopathological results. Disease-free survival (DFS) and overall survival (OS) were analyzed using 8-year follow-up data.

RESULTS

Postoperative histopathological analysis revealed that all patients had grade 1-2 EEC localized to the corpus uteri. Only three patients had deep myometrial invasion (stage IB), but they remained disease-free after 6-9 years of follow-up. The median follow-up time for all patients was 8.7 years. The DFS was 7.6 years, and the OS was 8.6 years. Two patients with stage IA grade 1 EEC experienced relapse and, despite treatment, died of EC. No other EC-related deaths occurred.

CONCLUSIONS

The screening algorithm accurately identified low-risk EC patients without compromising survival. Therefore, the algorithm appears to be feasible for selecting patients for surgery in secondary hospitals.

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Body oncologic imaging subspecialty training a curriculum based on the experience in a tertiary cancer center

PURPOSE

To describe the structure of a dedicated body oncologic imaging fellowship program. To summarize the numbers and types of cross-sectional imaging examinations reported by fellows.

METHODS

The curriculum, training methods, and assessment measures utilized in the program were reviewed and described. An educational retrospective analysis was conducted. Data on the number of examinations interpreted by fellows, breakdown of modalities, and examinations by disease management team (DMT) were collected.

RESULTS

A total of 38 fellows completed the fellowship program during the study period. The median number of examinations reported per fellow was 2296 [interquartile range: 2148 - 2534], encompassing all oncology-relevant imaging modalities: CT 721 [646-786], MRI 1158 [1016-1309], ultrasound 256 [209-320] and PET/CT 176 [130-202]. The breakdown of examinations by DMT revealed variations in imaging patterns, with MRIs most frequently interpreted for genitourinary, musculoskeletal, and hepatobiliary cancers, and CTs most commonly for general staging or assessment of nonspecific symptoms.

CONCLUSION

This descriptive analysis may serve as a foundation for the development of similar fellowship programs and the advancement of body oncologic imaging. The volume and diversity of examinations reported by fellows highlights the comprehensive nature of body oncologic imaging.

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

PURPOSE

To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC).

METHODS

Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction.

RESULTS

This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05).

CONCLUSION

A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

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 and intraoperative assessment of myometrial invasion in patients with FIGO stage I non-endometrioid endometrial carcinoma-a large-scale, multi-center, and retrospective study

INTRODUCTION

Myometrial invasion is a prognostic factor for lymph node metastases and decreased survival in non-endometrioid endometrial carcinoma patients. Herein, we explored the mode of myometrial invasion diagnosis in FIGO stage I non-endometrioid carcinoma and evaluated the differences in diagnostic efficiency among intraoperative frozen section (IFS), intraoperative gross examination (IGE), magnetic resonance imaging (MRI), and computed tomography (CT) in clinical practice. Finally, we suggested which test should be routinely performed.

METHOD

This was a historical cohort study nationwide with 30 centers in China between January 2000 and December 2019. Clinical data, including age, histology, method of myometrial invasion evaluation (MRI, CT, IGE, and IFS), and final diagnosis of postoperative paraffin sections, were collected from 490 non-endometrioid endometrial carcinoma (serous, clear cell, undifferentiated, mixed carcinoma, and carcinosarcoma) women in FIGO stage I.

RESULTS

Among the 490 patients, 89.59% presented myometrial invasion. The methods reported for myometrial invasion assessment were IFS in 23.47%, IGE in 69.59%, MRI in 37.96%, and CT in 10.20% of cases. The highest concordance was detected between IFS and postoperative paraffin sections (Kappa = 0.631, accuracy = 93.04%), followed by IGE (Kappa = 0.303, accuracy = 82.40%), MRI (Kappa = 0.131, accuracy = 69.35%), and CT (Kappa = 0.118, accuracy = 50.00%). A stable diagnostic agreement between IFS and the final results was also found through the years (2000-2012: Kappa = 0.776; 2013-2014: Kappa = 0.625; 2015-2016: Kappa = 0.545; 2017-2019: Kappa = 0.652).

CONCLUSION

In China, the assessment of myometrial invasion in non-endometrioid endometrial carcinoma is often performed via IGE, but the reliability is relatively low in contrast to IFS. In clinical practice, IFS is a reliable method that can help accurately assess myometrial invasion and intraoperative decision-making (lymph node dissection or not). Hence, it should be routinely performed in non-endometrioid endometrial carcinoma patients.

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.

A deep learning-based automatic staging method for early endometrial cancer on MRI images

Early treatment increases the 5-year survival rate of patients with endometrial cancer (EC). Deep learning (DL) as a new computer-aided diagnosis method has been widely used in medical image processing which can reduce the misdiagnosis by radiologists. An automatic staging method based on DL for the early diagnosis of EC will benefit both radiologists and patients. To develop an effective and automatic prediction model for early EC diagnosis on magnetic resonance imaging (MRI) images, we retrospectively enrolled 117 patients (73 of stage IA, 44 of stage IB) with a pathological diagnosis of early EC confirmed by postoperative biopsy at our institution from 1 January 2018, to 31 December 2020. Axial T2-weighted image (T2WI), axial diffusion-weighted image (DWI) and sagittal T2WI images from 117 patients have been classified into stage IA and stage IB according to the patient’s pathological diagnosis. Firstly, a semantic segmentation model based on the U-net network is trained to segment the uterine region and the tumor region on the MRI images. Then, the area ratio of the tumor region to the uterine region (TUR) in the segmentation map is calculated. Finally, the receiver operating characteristic curves (ROCs) are plotted by the TUR and the results of the patient’s pathological diagnosis in the test set to find the optimal staging thresholds for stage IA and stage IB. In the test sets, the trained semantic segmentation model yields the average Dice similarity coefficients of uterus and tumor on axial T2WI, axial DWI, and sagittal T2WI were 0.958 and 0.917, 0.956 and 0.941, 0.972 and 0.910 respectively. With pathological diagnostic results as the gold standard, the classification model on axial T2WI, axial DWI, and sagittal T2WI yielded an area under the curve (AUC) of 0.86, 0.85 and 0.94, respectively. In this study, an automatic DL-based segmentation model combining the ROC analysis of TUR on MRI images presents an effective early EC staging method.

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.

MRI-based radiomics model for distinguishing endometrial carcinoma from benign mimics: A multicenter study

PURPOSE

To develop and validate an MRI-based radiomics model for preoperatively distinguishing endometrial carcinoma (EC) with benign mimics in a multicenter setting.

METHODS

Preoperative MRI scans of EC patients were retrospectively reviewed and divided into training set (158 patients from device 1 in center A), test set #1 (78 patients from device 2 in center A) and test set #2 (109 patients from device 3 in center B). Two radiologists performed manual delineation of tumor segmentation on the map of apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI). The features were extracted and firstly selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) was employed to build the radiomics model, which is tuned in the training set using 10-fold cross-validation, and then assessed on the test set. Performance of the model was determined by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score.

RESULTS

Five most informative features are selected from the extracted 3142 features. The SVM with linear kernel was employed to build the radiomics model. The AUCs of the model were 0.989 (95% confidence interval [CI]: 0.968-0.997) for the training set, 0.999 (95% CI: 0.991-1.000) for test set #1, 0.961 (95% CI: 0.902-0.983) for test set #2. Accuracies of the model were 0.937 for the training set (precision: 0.919, recall: 0.963, F1-score: 0.940), 0.974 for test set #1 (precision: 0.949, recall: 1.000, F1-score: 0.974) and 0.908 for test set #2 (precision: 0.899, recall: 0.954, F1-score: 0.925). These results confirmed the efficacy of this model in predicting EC in different centers.

CONCLUSION

The MRI-based radiomics model showed promising potential for distinguishing EC with benign mimics and might be useful for surgical management of EC.

European Society of Gynaecological Oncology quality indicators for the surgical treatment of endometrial carcinoma

BACKGROUND

Quality of surgical care as a crucial component of a comprehensive multi-disciplinary management improves outcomes in patients with endometrial carcinoma, notably helping to avoid suboptimal surgical treatment. Quality indicators (QIs) enable healthcare professionals to measure their clinical management with regard to ideal standards of care.

OBJECTIVE

In order to complete its set of QIs for the surgical management of gynecological cancers, the European Society of Gynaecological Oncology (ESGO) initiated the development of QIs for the surgical treatment of endometrial carcinoma.

METHODS

QIs were based on scientific evidence and/or expert consensus. The development process included a systematic literature search for the identification of potential QIs and documentation of the scientific evidence, two consensus meetings of a group of international experts, an internal validation process, and external review by a large international panel of clinicians and patient representatives. QIs were defined using a structured format comprising metrics specifications, and targets. A scoring system was then developed to ensure applicability and feasibility of a future ESGO accreditation process based on these QIs for endometrial carcinoma surgery and support any institutional or governmental quality assurance programs.

RESULTS

Twenty-nine structural, process and outcome indicators were defined. QIs 1-5 are general indicators related to center case load, training, experience of the surgeon, structured multi-disciplinarity of the team and active participation in clinical research. QIs 6 and 7 are related to the adequate pre-operative investigations. QIs 8-22 are related to peri-operative standards of care. QI 23 is related to molecular markers for endometrial carcinoma diagnosis and as determinants for treatment decisions. QI 24 addresses the compliance of management of patients after primary surgical treatment with the standards of care. QIs 25-29 highlight the need for a systematic assessment of surgical morbidity and oncologic outcome as well as standardized and comprehensive documentation of surgical and pathological elements. Each QI was associated with a score. An assessment form including a scoring system was built as basis for ESGO accreditation of centers for endometrial cancer surgery.

Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation

The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.

Evaluation of the Depth of Myometrial Invasion of Endometrial Carcinoma: Comparison of Orthogonal Pelvis-axial Contrast-enhanced and Uterus-axial Dynamic Contrast-enhanced MRI Protocols

RATIONALE AND OBJECTIVES

To compare the diagnostic performance of orthogonal pelvis-axial (OPA) contrast-enhanced (CE) and orthogonal uterus-axial (OUA) dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) protocols in assessing the depth of myometrial invasion (MI) of endometrial carcinoma (EC).

MATERIALS AND METHODS

Preoperative MRI of 398 consecutive EC patients (197 patients with OPA CE-MRI protocol and 201 patients with OUA DCE-MRI protocol) was analyzed. Two radiologists independently interpreted the depth of MI, with postoperative histopathology as the reference standard. The chi-square test, Fisher's exact test, and receiver operating characteristic curve analysis were used for diagnostic performance comparison.

RESULTS

OUA DCE-MRI showed a significantly larger area under the curve than OPA CE-MRI in detecting the presence of MI for radiologist 1 (0.71 versus 0.49, p < 0.05) but not for radiologist 2 or deep MI (all p > 0.05). Compared to OPA CE-MRI, OUA DCE-MRI significantly improved the diagnostic accuracy of non-MI and superficial MI (radiologist 1: 45.5% versus 0 and 88.7% versus 86.4%, p = 0.045 and 0.567, respectively; radiologist 2: 45.5% versus 12.5% and 88.7% versus 78.8%, p = 0.177 and 0.027, respectively) and of EC with adenomyosis/submucous myomas, cornual tumor, and antero-posterior diameter ≤ 10 mm (radiologist 1: 86.4% versus 71.4%, 91.2% versus 67.7%, and 90.1% versus 81.1%, p = 0.048, 0.018, and 0.081, respectively; radiologist 2: 86.4% versus 64.3%, 88.2% versus 64.5%, and 87.0% versus 71.6%, p = 0.006, 0.023, and 0.019, respectively).

CONCLUSION

The OUA DCE-MRI protocol was superior to the OPA CE-MRI protocol in assessing the depth of MI of EC.

Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

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

Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study

RATIONALE AND OBJECTIVES

To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability.

MATERIALS AND METHODS

Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings.

RESULTS

Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48).

CONCLUSION

We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.

ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma

A European consensus conference on endometrial carcinoma was held in 2014 to produce multidisciplinary evidence-based guidelines on selected questions. Given the large body of literature on the management of endometrial carcinoma published since 2014, the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy & Oncology (ESTRO) and the European Society of Pathology (ESP) jointly decided to update these evidence-based guidelines and to cover new topics in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide. ESGO/ESTRO/ESP nominated an international multidisciplinary development group consisting of practicing clinicians and researchers who have demonstrated leadership and expertise in the care and research of endometrial carcinoma (27 experts across Europe). To ensure that the guidelines are evidence-based, the literature published since 2014, identified from a systematic search was reviewed and critically appraised. In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the development group. The guidelines are thus based on the best available evidence and expert agreement. Prior to publication, the guidelines were reviewed by 191 independent international practitioners in cancer care delivery and patient representatives. The guidelines comprehensively cover endometrial carcinoma staging, definition of prognostic risk groups integrating molecular markers, pre- and intra-operative work-up, fertility preservation, management for early, advanced, metastatic, and recurrent disease and palliative treatment. Principles of radiotherapy and pathological evaluation are also defined.

ESGO/ESTRO/ESP Guidelines for the management of patients with endometrial carcinoma

A European consensus conference on endometrial carcinoma was held in 2014 to produce multidisciplinary evidence-based guidelines on selected questions. Given the large body of literature on the management of endometrial carcinoma published since 2014, the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy & Oncology (ESTRO) and the European Society of Pathology (ESP) jointly decided to update these evidence-based guidelines and to cover new topics in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide. ESGO/ESTRO/ESP nominated an international multidisciplinary development group consisting of practicing clinicians and researchers who have demonstrated leadership and expertise in the care and research of endometrial carcinoma (27 experts across Europe). To ensure that the guidelines are evidence-based, the literature published since 2014, identified from a systematic search was reviewed and critically appraised. In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the development group. The guidelines are thus based on the best available evidence and expert agreement. Prior to publication, the guidelines were reviewed by 191 independent international practitioners in cancer care delivery and patient representatives. The guidelines comprehensively cover endometrial carcinoma staging, definition of prognostic risk groups integrating molecular markers, pre- and intra-operative work-up, fertility preservation, management for early, advanced, metastatic, and recurrent disease and palliative treatment. Principles of radiotherapy and pathological evaluation are also defined.

MRI to assess deep myometrial invasion in patients with endometrial cancer:A multi-reader study to evaluate the diagnostic role of different sequences

OBJECTIVE

The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol.

METHODS

In this multicenter retrospective study, MRI were independently evaluated by 4 radiologists (2 senior and 2 novice) with a sequence-based approach to identify DMI. The performance of the entire protocol was also evaluated. A comparison between the different sequences assessed by the same reader was performed using receiver operating curve and post-hoc analysis. Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-observer variability.

RESULTS

A total of 92 patients were included. The performance of the readers did not show significant differences among DWI, DCE and the entire protocol. For only one senior radiologist, who reached the highest diagnostic accuracy with the entire protocol (82,6 %), both DWI (p = 0,0197) and entire protocol (p = 0,0039) were found significantly superior to T2-w. The highest inter-observer agreement was obtained with the entire protocol by expert readers (ICC = 0,77).

CONCLUSIONS

For the detection of DMI, the performances of DWI and DCE alone and that of a complete protocol do not significantly differ, even though the latter ensures the highest reliability particularly for expert readers. In cases in which T2-w and DWI are consistent, an unenhanced protocol could be proposed.

ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma

A European consensus conference on endometrial carcinoma was held in 2014 to produce multi-disciplinary evidence-based guidelines on selected questions. Given the large body of literature on the management of endometrial carcinoma published since 2014, the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy and Oncology (ESTRO), and the European Society of Pathology (ESP) jointly decided to update these evidence-based guidelines and to cover new topics in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide.

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.

Diagnostic Performance of MRI for Assessing Parametrial Invasion in Cervical Cancer: A Head-to-Head Comparison between Oblique and True Axial T2-Weighted Images

OBJECTIVE

To directly compare the diagnostic performance of true and oblique axial T2-weighted imaging (T2WI) for assessing parametrial invasion (PMI) in cervical cancer.

MATERIALS AND METHODS

This retrospective study included 71 women with treatment-naive cervical cancer who underwent MRI that included both oblique and true axial T2WI, followed by radical hysterectomy. Two blinded radiologists (Radiologist 1 and Radiologist 2) independently assessed the presence of PMI on both sequences using a 5-point Likert scale. Receiver operating characteristic (ROC) curve analysis was performed, with a subgroup analysis for tumors sized > 2.5 cm and ≤ 2.5 cm in diameter. Inter-reader agreement was assessed with kappa (k) statistics.

RESULTS

At hysterectomy, 15 patients (21.1%) had PMI. For Radiologist 1, the area under the ROC curve (AUC) was greater for oblique axial than for true axial T2WI {0.941 (95% confidence interval [CI] = 0.858-0.983) vs. 0.917 (95% CI = 0.827-0.969), p = 0.027}. The difference was not significant for Radiologist 2 (0.879 [95% CI = 0.779-0.944] vs. 0.827 [95% CI = 0.719-0.906], p = 0.153). For tumors > 2.5 cm, AUC was greater with oblique than with true axial T2WI (0.906 vs. 0.860, p = 0.046 for Radiologist 1 and 0.839 vs. 0.765, p = 0.086 for Radiologist 2). Agreement between the radiologists was almost perfect for oblique axial T2WI (k = 0.810) and was substantial for true axial T2WI (k = 0.704).

CONCLUSION

Oblique axial T2WI potentially provides greater diagnostic performance than true axial T2WI for determining PMI, particularly for tumors > 2.5 cm. The inter-reader agreement was greater with oblique axial T2WI.

Second opinions in orthopedic oncology imaging: can fellowship training reduce clinically significant discrepancies?

OBJECTIVE

To determine factors that lead to significant discrepancies in second-opinion consultation of orthopedic oncology patients, and particularly if musculoskeletal fellowship training can decrease clinically significant discrepancies.

METHODS

A PACS database was queried for secondary reads on outside cross-sectional imaging studies, as requested by orthopedic oncology from 2014 to 2017. Comparison of original and secondary reports was performed using a published seven-point scale that defines clinically significant discrepancies. An online search was performed for each original radiologist to record if a fellowship in musculoskeletal imaging was completed. Additionally, years of post-residency experience, number of Medicare part B patients billed per year (marker of practice volume), and average hierarchical condition category for each radiologist (marker of practice complexity) was recorded.

RESULTS

A total of 571 patients met the inclusion criteria, with 184 cases initially interpreted by an outside fellowship trained musculoskeletal (MSK) radiologist and 387 cases initially interpreted by a non-MSK trained radiologist. The rate of clinically significant discrepancy was 9.2% when initially interpreted by MSK radiologists compared with 27.9% when initially performed by non-MSK radiologists (p < 0.05). After adjustment by both patient characteristics and radiologist characteristics, the likelihood of clinically significant discrepancies was greater for initial interpretations by non-MSK radiologists compared with MSK radiologists (OR = 1.36; 95% CI = 1.23-2.49).

CONCLUSION

In orthopedic oncology patients, the rate of clinically significant discrepancies was significantly higher when initially interpreted by non-MSK radiologists compared with MSK radiologists. The lower rate of clinically significant discrepancies demonstrates that subspecialty training may direct more appropriate diagnosis and treatment.

Global Challenges for Cancer Imaging

Imaging plays many essential roles in nearly all aspects of high-quality cancer care. However, challenges to the delivery of optimal cancer imaging in both developing and advanced countries are manifold. Developing countries typically face dramatic shortages of both imaging equipment and general radiologists, and efforts to improve cancer imaging in these countries are often complicated by poor infrastructure, cultural barriers, and other obstacles. In advanced countries, on the other hand, although imaging equipment and general radiologists are typically accessible, the complexity of oncologic imaging and the need for subspecialists in the field are largely unrecognized; as a result, training opportunities are lacking, and there is a shortage of radiologists with the necessary subspecialty expertise to provide optimal cancer care and participate in advanced clinical research. This article is intended to raise awareness of these challenges and catalyze further efforts to address them. Some promising strategies and ongoing efforts are reviewed, and some specific actions are proposed.

Conservative management of endometrial cancer: a survey amongst European clinicians

OBJECTIVE

To investigate differences and similarities in the clinical approach of young clinicians managing women with endometrial cancer (EC) conservatively.

METHODS

A web-based survey was carried out. A platform of the European Network of Young Gynaecological Oncologists (ENYGO) database was used. A 38-item multiple-choice questionnaire was used to evaluate current practice in fertility-sparing management of EC. The survey covered investigations, treatment options, follow-up and management of recurrence and future family planning. Descriptive statistics were used.

RESULTS

Overall, 116 out of 650 (17.84%) ENYGO members responded to the survey. In 92 (79.3%) centres, the caseload of early stage EC treated conservatively was less than 10 per year. One hundred and seven responders (93.8%) believe that treatment with progestins could be offered in grade 1 EC without myometrial invasion, but a minority would recommend it even for grade 2 tumours with no myometrial invasion or grade 1 with superficial invasion. The diagnostic tool for establishing grade of tumour was hysteroscopy with dilatation and curettage in 64 (55%) centres. Medroxyprogesterone acetate represents the most commonly prescribed progestogen (55, 47.4%). In 78 (67.2%) centres, a repeat endometrial biopsy was offered after 3 months of treatment commencement. Recurrences are treated mostly with hysterectomy (81, 69.9%) with only a small number of responders recommending to repeat progestin treatment. Lynch syndrome is a contraindication for conservative management in half of the responders (57, 49.1%). Most clinicians agree that patients should be referred promptly for assisted reproductive techniques once complete response has been achieved (68, 58.6%).

CONCLUSIONS

Our study shows that conservative management is increasingly offered to women affected by early stage EC wishing to preserve their fertility. Further studies and joint registries are required to evaluate safety and effectiveness of this approach in this probably growing number of patients.

Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to perform a head-to-head comparison between high-b-value (> 1000 s/mm²) and standard-b-value (800-1000 s/mm²) DWI regarding diagnostic performance in the detection of prostate cancer.

MATERIALS AND METHODS

The MEDLINE and EMBASE databases were searched up to April 1, 2017. The analysis included diagnostic accuracy studies in which high- and standard-b-value DWI were used for prostate cancer detection with histopathologic examination as the reference standard. Methodologic quality was assessed with the revised Quality Assessment of Diagnostic Accuracy Studies tool. Sensitivity and specificity of all studies were calculated and were pooled and plotted in a hierarchic summary ROC plot. Meta-regression and multiple-subgroup analyses were performed to compare the diagnostic performances of high- and standard-b-value DWI.

RESULTS

Eleven studies (789 patients) were included. High-b-value DWI had greater pooled sensitivity (0.80 [95% CI, 0.70-0.87]) (p = 0.03) and specificity (0.92 [95% CI, 0.87-0.95]) (p = 0.01) than standard-b-value DWI (sensitivity, 0.78 [95% CI, 0.66-0.86]); specificity, 0.87 [95% CI, 0.77-0.93] (p < 0.01). Multiple-subgroup analyses showed that specificity was consistently higher for high- than for standard-b-value DWI (p ≤ 0.05). Sensitivity was significantly higher for high- than for standard-b-value DWI only in the following subgroups: peripheral zone only, transition zone only, multiparametric protocol (DWI and T2-weighted imaging), visual assessment of DW images, and per-lesion analysis (p ≤ 0.04).

CONCLUSION

In a head-to-head comparison, high-b-value DWI had significantly better sensitivity and specificity for detection of prostate cancer than did standard-b-value DWI. Multiple-subgroup analyses showed that specificity was consistently superior for high-b-value DWI.