BACKGROUND Characterizing ovarian pathology is fundamental to optimizing management in both pre- and post-menopausal women. Inappropriate referral to oncology services can lead to unnecessary surgery or overly radical interventions compromising fertility in young women, whilst the consequences of failing to recognize cancer significantly impact on prognosis. By reflecting on recent developments of new diagnostic tests for preoperative identification of malignant disease in women with adnexal masses, we aimed to update a previous systematic review and meta-analysis. METHODS An extended search was performed in MEDLINE (PubMed) and EMBASE (OvidSp) from March 2008 to October 2013. Eligible studies provided information on diagnostic test performance of models, designed to predict ovarian cancer in a preoperative setting, that contained at least two variables. Study selection and extraction of study characteristics, types of bias, and test performance was performed independently by two reviewers. Quality was assessed using a modified version of the QUADAS assessment tool. A bivariate hierarchical random effects model was used to produce summary estimates of sensitivity and specificity with 95% confidence intervals or plot summary ROC curves for all models considered. RESULTS Our extended search identified a total of 1542 new primary articles. In total, 195 studies were eligible for qualitative data synthesis, and 96 validation studies reporting on 19 different prediction models met the predefined criteria for quantitative data synthesis. These models were tested on 26 438 adnexal masses, including 7199 (27%) malignant and 19 239 (73%) benign masses. The Risk of Malignancy Index (RMI) was the most frequently validated model. The logistic regression model LR2 with a risk cut-off of 10% and Simple Rules (SR), both developed by the International Ovarian Tumor Analysis (IOTA) study, performed better than all other included models with a pooled sensitivity and specificity, respectively, of 0.92 [95% CI 0.88-0.95] and 0.83 [95% CI 0.77-0.88] for LR2 and 0.93 [95% CI 0.89-0.95] and 0.81 [95% CI 0.76-0.85] for SR. A meta-analysis of centre-specific results stratified for menopausal status of two multicentre cohorts comparing LR2, SR and RMI-1 (using a cut-off of 200) showed a pooled sensitivity and specificity in premenopausal women for LR2 of 0.85 [95% CI 0.75-0.91] and 0.91 [95% CI 0.83-0.96] compared with 0.93 [95% CI 0.84-0.97] and 0.83 [95% CI 0.73-0.90] for SR and 0.44 [95% CI 0.28-0.62] and 0.95 [95% CI 0.90-0.97] for RMI-1. In post-menopausal women, sensitivity and specificity of LR2, SR and RMI-1 were 0.94 [95% CI 0.89-0.97] and 0.70 [95% CI 0.62-0.77], 0.93 [95% CI 0.88-0.96] and 0.76 [95% CI 0.69-0.82], and 0.79 [95% CI 0.72-0.85] and 0.90 [95% CI 0.84-0.94], respectively. CONCLUSIONS An evidence-based approach to the preoperative characterization of any adnexal mass should incorporate the use of IOTA Simple Rules or the LR2 model, particularly for women of reproductive age.

<b>BACKGROUND.</b> O-RADS ultrasound (US) and O-RADS MRI have been developed to standardize risk stratification of ovarian and adnexal lesions. <b>OBJECTIVE.</b> The purpose of this study was to perform a meta-analysis evaluating the diagnostic performance of O-RADS US and O-RADS MRI for risk stratification of ovarian and adnexal lesions. <b>EVIDENCE ACQUISITION.</b> We searched the Web of Science, PubMed, Cochrane Library, Embase, and Google Scholar databases from January 1, 2020, until October 31, 2022, for studies reporting on the performance of O-RADS US or O-RADS MRI in the diagnosis of malignancy of ovarian or adnexal lesions. Study quality was assessed with QUADAS-2. A hierarchic summary ROC model was used to estimate pooled sensitivity and specificity. Heterogeneity was assessed with the <i>Q</i> statistic. Metaregression analysis was performed to explore potential sources of heterogeneity. O-RADS US was compared with the International Ovarian Tumor Analysis (IOTA) simple rules and Assessment of Different Neoplasias in the Adnexa (ADNEX) model in studies providing head-to-head comparisons. <b>EVIDENCE SYNTHESIS.</b> Twenty-six studies comprising 9520 patients were included. O-RADS US was evaluated in 15 and O-RADS MRI in 12 studies; both systems were evaluated in one of the studies. Quality assessment revealed that risk of bias or concern about applicability most commonly related to patient selection. Pooled sensitivity and specificity of O-RADS US were 95% (95% CI, 91-97%) and 82% (95% CI, 76-87%) and of O-RADS MRI were 95% (95% CI, 92-97%) and 90% (95% CI, 84-94%). Analysis with the <i>Q</i> statistic revealed significant heterogeneity among studies of O-RADS US in both sensitivity and specificity (both <i>p</i> < .001) and among studies of O-RADS MRI in specificity (<i>p</i> < .001) but not sensitivity (<i>p</i> = .07). In metaregression, no factor was significantly associated with sensitivity or specificity of either system (all <i>p</i> > .05). O-RADS US showed no significant difference in sensitivity or specificity versus IOTA simple rules in four studies (sensitivity, 96% vs 93%; specificity, 76% vs 82%) or versus the ADNEX model in three studies (sensitivity, 96% vs 96%; specificity, 79% vs 78%). <b>CONCLUSION.</b> O-RADS US and O-RADS MRI both have high sensitivity for ovarian or adnexal malignancy. O-RADS MRI, but not O-RADS US, also has high specificity. <b>CLINICAL IMPACT.</b> Awareness of the diagnostic performance results regarding O-RADS US and O-RADS MRI will be helpful as these systems are increasingly implemented into clinical practice.

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.

OBJECTIVE To develop a computerized diagnostic model to characterize the ovarian cyst at their early stage in order to avoid unnecessary biopsy and patient anxiety. BACKGROUND The main cause of mortality and infertility in women is Ovarian Cancer. It is very difficult to diagnose ovarian cancer using Ultrasonography as benign and malignant ovarian masses or cysts exhibit similar characteristics. Early prediction and characterization of ovarian masses will reduce the unwanted growth of the ovarian mass. MATERIALS AND METHODS Transvaginal 2D B mode ovarian mass ultrasound images were preprocessed initially to enhance the image quality. And then, region of interest(ROI) in this case ovarian cyst is segmented. Finally, Local Binary Pattern (LBP) textural features were extracted. A Support Vector Machine was trained to classify the ovarian cyst or mass as benign or malignant. RESULTS The performance of the SVM is improved with an average accuracy of 92% when the textural features were extracted from the Original Gray Value based LBP(OGV-LBP) image than the histogram based LBP. CONCLUSION The SVM can classify the transvaginal 2D B mode ovarian cyst ultrasound images into benign and malignant effectivelywhen the textural features from the original gray value based LBP extracted were considered.

PURPOSE To assess the diagnostic accuracy of O-RADS Ultrasound (O-RADS US) v2022, O-RADS US v2020, and IOTA SR, and to evaluate whether combining imaging findings with tumor markers enhances the diagnosis of adnexal masses. METHODS This retrospective study, conducted between January 2018 and December 2023, included consecutive women with adnexal masses scheduled for surgery. Histopathologic results served as the reference standard. Risk factors for malignancy were identified using univariate and multivariate logistic regression analyses. ROC analysis was employed to assess diagnostic test performances, while Kappa statistics evaluated inter-reviewer agreement. RESULTS A total of 613 women (mean age, 49.39 ± 12.81 years; range, 16-87 years) with pelvic masses were included. O-RADS US v2022 exhibited comparable performance to O-RADS US v2020, with areas under the curve (AUC) values of 0.940 and 0.937, respectively (p = 0.02, exceeding the adjusted significance level of 0.0167). Both O-RADS models outperformed the IOTA SR, which had an AUC of 0.862 (p < 0.0001 for both comparisons). Multivariate analysis revealed that O-RADS US v2022 [OR 9.148, 95 %CI (4.912-17.039), p < 0.001] and HE4 [OR 1.023, 95 %CI (1.010-1.036), p = 0.001] were significant factors associated with malignant lesions. Furthermore, the combination of O-RADS US v2022 and HE4 demonstrated an AUC of 0.98, significantly outperforming either O-RADS US v2022 alone (AUC = 0.94) or HE4 alone (AUC = 0.92). The Kappa values for O-RADS US v2022, O-RADS US v2020 and IOTA SR were 0.933, 0.891 and 0.923, respectively, indicating substantial inter-reader agreement. CONCLUSIONS The O-RADS US v2022 demonstrates comparable performance in predicting ovarian malignant lesions when compared to O-RADS US v2020, while surpassing the performance of IOTA SR. Additionally, the combination of O-RADS US v2022 and HE4 provides improved diagnostic effectiveness over using either O-RADS US v2022 or HE4 alone.

Mesenteric cysts in pregnancy. A case report. Mesenteric cysts are rare. These large, often asymptomatic growths may be misinterpreted easily by the clinician as representing either benign or malignant ovarian tumors, renal masses, or hepatic tumors or cysts. We treated a women for a mesenteric cyst complicating her second pregnancy. Because of the increasing use of ultrasound in a wide variety of pregnancy complications, and because of the mesenteric cysts's unique sonographic appearance (resembling that of a benign ovarian tumor), such a cyst must be included in the differential diagnosis of large, cystic abdominal masses in pregnancy. Published in The Journal of reproductive medicine (1989)

Macroscopic characterization of ovarian tumors and the relation to the histological diagnosis: criteria to be used for ultrasound evaluation. Ultrasound is now frequently used for evaluation of pathological findings discovered on gynecological examination and for puncture of ovarian cysts. Although the new, high-frequency vaginal transducers have a very high resolution, only macroscopically visible structures of the tumors can be imaged. For this reason, it seemed important to classify ovarian tumors according to their macroscopic appearance and then relate this to whether the tumor was benign, borderline, or malignant. Such a classification has not been performed before. Medical records from women operated upon due to pelvic tumors over a period of 11 years were scrutinized. There were 1017 women included in the study. Among those tumors characterized as unilocular cysts 0.3% (1/296) was malignant; this tumor had macroscopically visible papillary vegetations on the inside of the cyst wall. This cyst was found in a woman 60 years old. Sixty percent (178/296) of the women who had a unilocular cyst were over the age of 40.

Distinction of benign from malignant ovarian cysts by ultrasound. The ultrasonic characteristics of benign and malignant cystic ovarian tumours have been reviewed. The value of each of eight different ultrasonic features has been assessed and the specificity of each for confirmation or exclusion of malignancy is indicated. In combination they permit correct differentiation in 91 percent of cases. Published in British journal of obstetrics and gynaecology (1978)

also result in cysts. Rarely, cysts may be a form of ovarian cancer. Diagnosis is undertaken by pelvic examination with a pelvic ultrasound or other testing An ovarian cyst is a fluid-filled sac within the ovary. They usually cause no symptoms, but occasionally they may produce bloating, lower abdominal pain, or lower back pain. The majority of cysts are harmless. If the cyst either breaks open or causes twisting of the ovary, it may cause severe pain. This may result in vomiting or feeling faint, and even cause headaches. Most ovarian cysts are relat An… RMI 2 is regarded as more sensitive than RMI 1, but the model has low specificity, which means that many of the suspected cancers turn out to be overdiagnosed benign cysts. The calculation is often inaccurate during pregnancy, especially when CA-125 levels peak towards the end of the first trimester. The International Ovarian Tumor Analysis (IOTA) group has produced a different model. Theirs relies on "simple descriptors" and "simple rules". An example of a simple descriptor for a benign cyst is "Unilocular cyst of anechoic content with regular walls and largest diameter less than 10 cm". An example of a simple rule is acoustic shadows are associated with benign cysts. Although most cases of ovarian cysts are monitored and stabilize or resolve without surgery, some cases require surgery. Common indications for surgical management include ovarian torsion, ruptured cyst, concerns that the cyst is cancerous, and pain; some surgeons additionally recommend removing all large cysts. The surgery may involve removing the cyst alone, or one or both ovaries. Very large, potentially cancerous, and recurrent cysts, particularly in menopausal women, are more likely to be treated by removing the affected ovary, or both the ovary and its Fallopian tube (salpingo-oophorectomy). For women of reproductive age, the aim is to preserve as much of the reproductive system as possible. It's often possible to just remove the cyst and leave both ovaries intact, which means the fertility should be unaffected. Simple benign cysts can be drained through fine-needle aspiration. However, the risk of recurrence is fairly high (33–40%), and if a cancerous tumor was misdiagnosed, it could cause the cancer to spread. The surgical technique is typically a minimally invasive or laparoscopic approach performed under general anaesthesia, unless the cyst is particularly large (e.g., 10 cm [4 inches] in diameter), or if pre-operative imaging, such as pelvic ultrasound, suggests malignancy or complex anatomy. For large cysts, open laparotomy or a mini-laparotomy (a smaller incision through the abdominal wall) may be preferred. Minimally invasive surgeries are not used when ovarian cancer is suspected.…