Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. METHODS: A retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. RESULTS: The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85-0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65-23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06-0.19). The DCA curve demonstrated the practical clinical utility of the model. CONCLUSIONS: The deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

The Effect of Hormonal Treatment on Ovarian Endometriomas: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the effect of hormonal suppression of endometriosis on the size of endometriotic ovarian cysts. DATA SOURCES: The authors searched MEDLINE, PubMed, Cochrane Central Register of Controlled Trials, Embase, and ClinicalTrials.gov from January 2012 to December 2022. METHODS OF STUDY SELECTION: We included studies of premenopausal women undergoing hormonal treatment of endometriosis for ≥3 months. The authors excluded studies involving surgical intervention in the follow-up period and those using hormones to prevent endometrioma recurrence after endometriosis surgery. Risk of bias was assessed with the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. The protocol was registered in PROSPERO (CRD42022385612). TABULATION, INTEGRATION, AND RESULTS: The primary outcome was the mean change in endometrioma volume, expressed as a percentage, from baseline to at least 6 months. Secondary outcomes were the change in volume at 3 months and analyses by class of hormonal therapy. The authors included 16 studies (15 cohort studies, 1 randomized controlled trial) of 888 patients treated with dienogest (7 studies), other progestins (4), combined hormonal contraceptives (2), and other suppressive therapy (3). Globally, the decrease in endometrioma volume became statistically significant at 6 months with a mean reduction of 55% (95% confidence interval, -40 to -71; 18 treatment groups; 730 patients; p <.001; I2 = 96%). The reduction was the greatest with dienogest and norethindrone acetate plus letrozole, followed by relugolix and leuprolide acetate. The volume reduction was not statistically significant with combined hormonal contraceptives or other progestins. There was high heterogeneity, and studies were at risk of selection bias. CONCLUSION: Hormonal suppression can substantially reduce endometrioma size, but there is uncertainty in the exact reduction patients may experience.

Ultrasound image-based deep learning to differentiate tubal-ovarian abscess from ovarian endometriosis cyst.

Objectives: We developed ultrasound (US) image-based convolutional neural networks (CNNs) to distinguish between tubal-ovarian abscess (TOA) and ovarian endometriosis cyst (OEC). Methods: A total of 202 patients who underwent US scanning and confirmed tubal-ovarian abscess or ovarian endometriosis cyst by pathology were enrolled in retrospective research, in which 171 patients (from January 2014 to September 2021) were considered the primary cohort (training, validation, and internal test sets) and 31 patients (from September 2021 to December 2021) were considered the independent test cohort. There were 68 tubal-ovarian abscesses and 89 OEC, 4 TOA and 10 OEC, and 10 TOA and 21 OEC patients belonging to training and validation sets, internal sets, and independent test sets, respectively. For the model to gain better generalization, we applied the geometric image and color transformations to augment the dataset, including center crop, random rotation, and random horizontal flip. Three convolutional neural networks, namely, ResNet-152, DenseNet-161, and EfficientNet-B7 were applied to differentiate tubal-ovarian abscess from ovarian endometriosis cyst, and their performance was compared with three US physicians and a clinical indicator of carbohydrate antigen 125 (CA125) on the independent test set. The area under the receiver operating characteristic curves (AUROCs) of accuracy, sensitivity, and specificity were used to evaluate the performance. Results: Among the three convolutional neural networks, the performance of ResNet-152 was the highest, with AUROCs of 0.986 (0.954-1). The AUROCs of the three physicians were 0.781 (0.620-0.942), 0.738 (0.629-848), and 0.683 (0.501-0.865), respectively. The clinical indicator CA125 achieved only 0.564 (0.315-0.813). Conclusion: We demonstrated that the CNN model based on the US image could discriminate tubal-ovarian abscess and ovarian endometriosis cyst better than US physicians and CA125. This method can provide a valuable predictive reference for physicians to screen tubal-ovarian abscesses and ovarian endometriosis cysts in time.

A case report of an endometriosis cyst at cesarean scar defect and review of literature.

BACKGROUND: Cesarean scar defect (CSD) presents as a cystic defect that connects the uterine cavity at the site of the previous cesarean section (CS). Endometriosis refers to the discovery of endometrial glands and stroma outside the uterine cavity. Cases of endometriosis cysts at CSD have not been reported. CASE PRESENTATION: In this article, we will present a patient with an endometriosis cyst at CSD with symptoms of a prolonged menstrual cycle, periods without cyclic abdominal pain, and a history of cesarean delivery. The gynecologic ultrasound showed a CSD and a mixed mass in the right front of the uterus. After about 1 month, the tumor grew from a diameter of 4.75 cm to 8.06 × 6.23 × 3.66 cm. The patient eventually had an operation, which revealed a mass protruding from the incision in the anterior uterine wall, which was attached to the anterior uterine wall by a thin tip with a smooth surface. Intraoperative rapid cytopathology suggested that endometrial glands were seen within the smooth muscle tissue, similar to endometriosis. Subsequently, the patient underwent resection of the endometriotic cyst. Final paraffin pathology showed smooth muscle with visible endometrial glands and old hemorrhage, and a one-year follow-up showed no recurrence of endometriosis cysts at CSD. CONCLUSIONS: Endometriosis cysts at CSD are very rare. The clinical symptoms may be less obvious, and the diagnosis relies mainly on the patient's previous surgical history and imaging. A finding of a pelvic mass in the location of the CSD, with or without symptoms of menstrual changes and intermittent abdominal pain, should be considered an endometriotic cyst at CSD. Surgical treatment is a good choice for this disease. Further studies are needed regarding the etiological mechanism of this case and why the mass enlarged rapidly in one mouth.