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.

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the ‘iKnife’). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

Background: Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.Purpose: To identify clinical features and imaging signatures on T2‐weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study Type: Retrospective.Population: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence: 1.5‐T and 3‐T scanners with sagittal T2‐weighted spin echo sequence.Assessment: Tumor regions were manually segmented on T2‐weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical Tests: A likelihood‐ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.Results: Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively.Data Conclusion: The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer.Evidence Level: 4Technical Efficacy: Stage 2

Introduction. Many factors have been suggested that influence a couple's decision to stop infertility treatment despite not achieving their goal of having a baby. This internet-based questionnaire study aims to shed light on these reasons by surveying patients who have received treatment from a variety of different centres. Methods. Registered users of an independent infertility website were invited to participate in the survey by filling in a structured questionnaire. Results. Eighty users completed the questionnaire. Fifty-eight percent of patients received treatment in just one centre. Seventy-eight percent of the patients paid for the treatment themselves. Forty-two percent of couples intended to have only one treatment cycle and 60% of couples reported undergoing more cycles than they had originally intended. Ten percent of patients regretted not stopping their treatment earlier. The most common reasons why patients opted against further treatment cycles were financial constraints (46%), emotional burden (35%), poor response to treatment (29%) and poor egg quality (19%). Conclusions. The decision to stop treatment despite poor outcomes is a difficult one and unique to the couple. The findings of this study are consistent with the widely held belief that in vitro fertilization is a stressful, expensive process given that almost all women cited these as reasons to stop treatment.