Risk for Advanced-Stage Endometrial Cancer in Surgical Specimens from Patients with Complex Endometrial Hyperplasia with Atypia

2012 ◽  
Vol 73 (1) ◽  
pp. 38-42 ◽  
Author(s):  
Abeer Eddib ◽  
Baraa Allaf ◽  
Jenny Lee ◽  
John Yeh
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Hyperplasia ◽  
Advanced Stage ◽  
Endometrial Hyperplasia With Atypia

scholarly journals Upregulation of Serum lncRNA DLEU1 Predicts Progression of Premalignant Endometrial Lesion and Unfavorable Clinical Outcome of Endometrial Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382096558
Author(s):  
Lixia Shan ◽  
Tao Zhao ◽  
Yu Wang
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Hyperplasia ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Critical Role ◽  
Disease Free Survival ◽  
Lymphocytic Leukemia ◽  
Healthy Controls ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective: Long non-coding RNAs (lncRNAs) play a critical role in tumorigenesis. Upregulation of lncRNA deleted in lymphocytic leukemia 1 (DLEU1) has been reported in endometrial cancer (EC) tissues. This prospective study aimed to determine the potential clinical significance of serum lncRNA DLEU1 in EC. Methods: The serum lncRNA DLEU1 level was detected in EC patients, patients with endometrial hyperplasia and healthy controls by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Then its clinical value in EC was further evaluated. Results: Our results demonstrated that serum lncRNA DLEU1 levels were significantly increased in patients with EC, and serum lncRNA DLEU1 showed good performance for discriminating EC patients from patients with endometrial hyperplasia and healthy controls. In addition, EC patients with advanced clinicopathological features had higher circulating lncRNA DLEU1 level than those with favorable clinical characteristics. Moreover, EC patients in the high serum lncRNA DLEU1 group suffered worse overall survival and disease-free survival than those in the low serum lncRNA DLEU1 group. Furthermore, multivariate cox regression analysis displayed that the serum lncRNA DLEU1 served as an independent prognostic factor for EC. Conclusions: Collectively, our study suggests that serum lncRNA DLEU1 is a novel and promising biomarker for prognostic estimation of EC.

scholarly journals Risk Assessment of Endometrial Hyperplasia or Endometrial Cancer with Simplified Ultrasound-Based Scoring Systems

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 442
Author(s):  
Norbert Stachowicz ◽  
Agata Smoleń ◽  
Michał Ciebiera ◽  
Tomasz Łoziński ◽  
Paweł Poziemski ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Hyperplasia ◽  
Endometrial Thickness ◽  
Transvaginal Ultrasound ◽  
Menopausal Status ◽  
Power Doppler ◽  
Uterine Cavity ◽  
Abnormal Uterine Bleeding ◽  
Scoring Systems ◽  
Diagnostic Challenge

Background: Abnormal uterine bleeding (AUB) represents a common diagnostic challenge, as it might be related to both benign and malignant conditions. Endometrial cancer may not be detected with blind uterine cavity sampling by dilatation and curettage or suction devices. Several scoring systems using different ultrasound image characteristics were recently proposed to estimate the risk of endometrial cancer (EC) in women with AUB. Aim: The aim of the present study was to externally validate the predictive value of the recently proposed scoring systems including the Risk of Endometrial Cancer scoring model (REC) for EC risk stratification. Material and methods: It was a retrospective cohort study of women with postmenopausal bleeding. From June 2012 to June 2020 we studied a group of 394 women who underwent standard transvaginal ultrasound examination followed by power Doppler intrauterine vascularity assessment. Selected ultrasound features of endometrial lesions were assessed in each patient. Results: The median age was 60.3 years (range ±10.7). The median body mass index (BMI) was 30.4 (range ± 6.0). Histological examination revealed 158 cases of endometrial hyperplasia (EH) and 236 cases of EC. Of the studied ultrasound endometrial features, the highest areas under the curve (AUCs) were found for endometrial thickness (ET) (AUC = 0.76; 95% CI: 0.71–0.81) and for interrupted endomyometrial junction (AUC = 0.70, 95% CI: 0.65–0.75). Selected scoring systems presented moderate to good predictive performance in differentiating EC and EH. The highest AUC was found for REC model (AUC = 0.75, 95% CI: 0.70–0.79) and for the basic model that included ET, Doppler score and interrupted endometrial junction (AUC = 0.77, 95% CI: 0.73–0.82). REC model was more accurate than other scoring systems and selected single features for differentiating benign hyperplasia from EC at early stages, regardless of menopausal status. Conclusions: New scoring systems, including the REC model may be used in women with AUB for more efficient differentiation between benign and malignant conditions.

scholarly journals Deep learning model for classifying endometrial lesions

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
YunZheng Zhang ◽  
ZiHao Wang ◽  
Jin Zhang ◽  
CuiCui Wang ◽  
YuShan Wang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Hyperplasia ◽  
Histogram Equalization ◽  
Atypical Hyperplasia ◽  
Endometrial Polyps ◽  
Submucous Myoma ◽  
Objective Model ◽  
Medical University ◽  
Deep Learning Model ◽  
Better Than

Abstract Background Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input. Methods All histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model’s performance. Thereafter, we compared the model’s results with the diagnosis of gynecologists. Results The overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model’s specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model’s accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved. Conclusions The VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.

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