An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer.

PURPOSE: To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). MATERIALS AND METHODS: A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. RESULTS: Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). CONCLUSION: The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.

An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer.

OBJECTIVES: To investigate the prognostic role of magnetic resonance imaging (MRI)-based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer. METHODS: A total of 207 cervical cancer patients (training cohort: n = 144; validation cohort: n = 63) were enrolled. 792 radiomics features were extracted from T2W and diffusion-weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps. RESULTS: A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS (p < 0.05) and DFS (p < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3-year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics. CONCLUSIONS: The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application. ADVANCES IN KNOWLEDGE: This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early-stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.

A prospective randomized controlled trial comparing two different treatments of intrauterine adhesions.

RESEARCH QUESTION: Intrauterine adhesions (IUA) are primarily caused by trauma to the endometrium, and hysteroscopy is presently the main treatment for IUA. However, high rates of post-operative adhesion re-formation remain a problem. In this study, the combination of an intrauterine device (IUD) with a Foley catheter and the balloon uterine stent were investigated to evaluate their efficacy in preventing adhesion re-formation and the subsequent reproductive outcomes in patients with moderate to severe adhesions. DESIGN: A prospective randomized controlled study was conducted in a university-affiliated hospital. A total of 171 women with Asherman's syndrome were initially recruited between August 2016 and December 2017 and were randomized to undergo either balloon uterine stent insertion or placement of a contraceptive IUD plus a Foley catheter after hysteroscopic adhesiolysis. Reduction of adhesion scores, incidence of adhesion re-formation, changes in menstrual flow and reproductive outcomes were analysed. RESULTS: A total of 118 participants were eligible for analysis. The American Fertility Society (AFS) scores were not significantly different between groups before hysteroscopic adhesiolysis. At the second-look hysteroscopy, the AFS scores and adhesion recurrence rates were significantly higher in the balloon uterine stent group compared with the combination group (P < 0.01 and P = 0.024, respectively). There were no statistically significant differences in pregnancy and live birth rates between the two groups. CONCLUSIONS: The combination of an IUD and a Foley balloon catheter had better efficacy in preventing adhesion re-formation than the balloon uterine stent alone; however, it did not produce better reproductive outcomes.

Nomogram Predicting Overall Survival in Operable Cervical Cancer Patients.

OBJECTIVE: Nomograms are widely used as predictive tools to predict oncological outcomes intuitively and precisely. The aim of our study was to develop a nomogram for predicting overall survival (OS) of patients with early stage cervical cancer. METHODS: In this retrospective study, the clinical, pathological, and hematological data and prognosis of 795 cervical cancer patients were investigated. We identified and incorporated independent significant prognostic factors for OS to develop a nomogram. The predictive accuracy and discriminative ability were measured by concordance index. RESULTS: By univariable analysis and subsequent multivariable analysis, we identified body mass index, albumin, platelet, leukocyte, tumor differentiation, and the status of the pelvic lymph node (PLN) (all P < 0.05) as independent prognostic factors. The concordance index of the nomogram integrating these 6 variables was 0.74. The calibration curves for probability of 3- and 5-year OS also demonstrated ideal agreement between nomogram prediction and actual observation. CONCLUSIONS: We developed a novel nomogram that can provide prediction of OS for patients with early stage cervical cancer individually. Furthermore, studies are required to validate whether it can be applied to other cohorts.