Feasibility, accuracy and advantages of frozen section examination of ovarian masses with normal tumor markers. A tertiary referral center experience

Objectives: To assess the diagnostic accuracy of different risk of malignancy index (RMI) scores and to evaluate the role of a modified RMI (RMI 5) in pre-operative discrimination between benign and malignant ovarian masses. Study Design: Prospective observational study. Patients and methods: Women with a suspicious ovarian mass scheduled for laparotomy or laparoscopy were potentially eligible for inclusion in the current study. Trans-abdominal and trans-vaginal ultrasound with Doppler assessment of the adnexal masses was done. Calculation of the RMI 1, RMI2, RMI 3, RMI 4, and RMI5 was done. We compared RMI to histopathological outcome. In the current study, a new RMI score was created by adding Doppler blood flow of the ovarian mass to the calculation of the previous RMI1. Results: One hundred and fifty women with ovarian masses were included in the current study. Ninety six women (64%) had benign ovarian masses while, malignant ovarian masses were found in 54 women (36%). Comparison between benign and malignant ovarian masses regarding to the risk of malignancy indices revealed that that there was a statistically significant difference between the two groups regarding to the risk of malignancy indices with Pvalue< 0.001. Receiver operator characteristic curve analysis of the 5 RMI indices shows that the best method for prediction of malignant ovarian tumor was RMI 1. Also there was no statistically significant difference between the five methods in prediction of malignant ovarian tumors. RMI5 with cut off value of 250 is reliable tool at a tertiarycenter to discriminate between ovarian cancer and benign ovarian masses with a sensitivity of 90.38% and specificity of 93.88%. There was statistically significant difference between the different stages of ovarian cancer and RMI 5 with (P<0.05). Conclusion: The RMI 1 is the gold standard for preoperative discrimination between benign and malignant ovarian masses. Adding Doppler flow to the parameters of RMI 1 (RMI 5) increased specificity of RMI 1 in detecting malignant ovarian masses.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

ObjectivesThe use of magnetic resonance (MR) imaging in differentiation between benign and malignant adnexal masses in children and adolescents might be of great value in the diagnostic workup of sonographically indeterminate masses, since preserving fertility is of particular importance in this population. This systematic review evaluates the diagnostic value of MR imaging in children with an ovarian mass.MethodsThe review was made according to the PRISMA Statement. PubMed and EMBASE were systematically searched for studies on the use of MR imaging in differential diagnosis of ovarian masses in both adult women and children from 2008 to 2018.ResultsSixteen paediatric and 18 adult studies were included. In the included studies, MR imaging has shown good diagnostic performance in differentiating between benign and malignant ovarian masses. MR imaging techniques including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to further improve the diagnostic performance.ConclusionThe addition of DWI with apparent diffusion coefficient (ADC) values measured in enhancing components of solid lesions and DCE imaging may further increase the good diagnostic performance of MR imaging in the pre-operative differentiation between benign and malignant ovarian masses by increasing specificity. Prospective age-specific studies are needed to confirm the high diagnostic performance of MR imaging in children and adolescents with a sonographically indeterminate ovarian mass.Key Points• MR imaging, based on several morphological features, is of good diagnostic performance in differentiating between benign and malignant ovarian masses. Sensitivity and specificity varied between 84.8 to 100% and 20.0 to 98.4%, respectively.• MR imaging techniques like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to improve the diagnostic performance.• Specific studies in children and adolescents with ovarian masses are required to confirm the suggested increased diagnostic performance of DWI and DCE in this population.

Background: As ovarian malignancies are one of the commonest malignancies in female population, timely and accurate diagnosis helps in early treatment resulting in better survival. Ultrasound is easily available diagnostic tool not only to diagnose but also accurately distinguish malignant from benign ovarian masses. Aims and Objectives: To evaluate sensitivity, specificity and accuracy of ultrasound in diagnosing and differentiating benign from malignant ovarian masses in comparison with histopathological findings. Materials and Methods: A prospective study was carried out from August 2015 to August 2018 for a period of 3 years. Total 150 patients with ovarian masses who were operated in our hospital and their final histopathological reports were available, were included in our study. Ultrasound diagnosis and histopathological diagnosis were compared. Results: Sensitivity, specificity and accuracy of ultrasound in diagnosing and differentiating malignant from benign ovarian masses were found to be 78.94%, 98.47% and 88.23% respectively compared with histopathological findings. Conclusion: Ultrasound is very sensitive, specific and accurate in not only diagnosing ovarian mass but also in differentiating malignant from benign entities making it invaluable and important diagnostic tool in evaluation of ovarian masses.

To apply the International Ovarian Tumor Analysis (IOTA) predictive models, the logistic regression model 2 (LR2) and the IOTA Assessment of Different NEoplasias in the adneXa (ADNEX), in patients with ovarian masses and to compare their performance in preoperative discrimination between benign and malignant adnexal lesions. This was a retrospective diagnostic accuracy study with prospectively collected data, performed between January 2019 and December 2022, in a single tertiary gynecologic oncology center in Greece. The study included women with an adnexal lesion which underwent surgery within 6months after of using the LR2 and ADNEX protocol to assess the risk of malignancy. Correlation of the ultrasound findings with the postoperative histopathological analysis was performed. Receiver-operating characteristics (ROC) curve analysis was used to determine the diagnostic accuracy of the models to classify tumors; sensitivity and specificity were determined for each model and their performance was compared. Of the136 participants, 117 (86%) had benign ovarian masses and 19 (14%) had malignant tumors. The area under the ROC curve (AUC) of the LR2 model was 0.84 (95% CI 0.74-0.93), which was significantly higher than the AUC for ADNEX model: 0.78 (95% CI 0.67-0.89). At a cut off > 10%, the LR2 model had the highest sensitivity 89.5% (95% CI 66.9-98.7) and specificity 85.1% (95% CI 76.9-91.2) compared to ADNEX model [sensitivity 84.2% (95% CI 60.4-96.6) and specificity 71.8% (95% CI 62.7-79.7)]. IOTA LR2 had the highest accuracy in differentiating between benign and malignant ovarian masses. IOTA LR2 and ADNEX models were both useful tools in discriminating between benign and malignant ovarian masses.

Ovarian cancer is a common female malignancy frequently identified at advanced stages. Diffusion-weighted imaging (DWI) provides valuable information on structural traits of tissue and is used as an imaging biomarker in OST cancer prognosis. Post-processing of three-dimensional apparent diffusion coefficient (ADC) maps has proven useful in evaluating variable tumors, although its position in ovarian cancer prognosis is until now not well defined. Consequently, our foremost objective was to assess the sensitivity and efficiency of DWI (T1 and T2) and ADC maps in malignant and benign ovarian lesions prognosis. A total of 58 patients with undetermined ovarian masses in ultrasound were referred to MRI for more accurate diagnosis. The signals of DWI (qualitative) and ADC values (quantitative DWI) of the lesion components were analyzed separately. Student's t-test and receiver operating characteristic (ROC) curves were used to determine the ability of DWI and ADC in the discrimination between malignant and benign ovarian masses. Of the 58 masses, 33 have been benign, and 25 have been malignant. There was a decrease correlation between signal thing on T2W and ADC values in malignant as compared to benign masses. The DWI and T1 + GAD values in malignant tumors have been substantially higher than the ones in benign masses (P value < 0.0001). Additionally, our consequences suggest that a T1 cutoff value (1 × 10⁻≥ mm²/s) would possibly the quality factor to help discriminate between benign and malignant lesions. The mixture of DWI imaging with T1 + GAD values can beautify the diagnostic overall performance in discrimination among benign and malignant ovarian masses by increasing specificity.

To compare the efficacy of the International Ovarian Tumor Analysis (IOTA) simple rules versus pattern recognition to differentiate between benign and malignant ovarian masses. A prospective cross-sectional study conducted at Kasr El Aini Hospital, Cairo, between April 2016 and October 2018 of 396 women with ovarian masses measuring more than 5cm who were candidates for surgery. All patients underwent two-dimensional transvaginal sonography: level 2 with IOTA simple rules followed by level 3 with pattern recognition. Patients subsequently underwent ovarian cystectomy or oophorectomy and the specimens were examined histopathologically. Accuracy was measured by comparing sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. IOTA simple rules specified 44/50 cases as malignant and 220/242 as benign (sensitivity and specificity of 88.0% and 90.9%, respectively). Pattern recognition identified 83/94 cases as malignant and 281/302 as benign (sensitivity and specificity of 88.3% and 92.7%, respectively). No statistically significant difference in accuracy was found between the two methods. IOTA simple rules are an effective tool for detecting ovarian malignancy when performed by nonexpert sonographers. When results are inconclusive, pattern recognition should be performed additionally by an expert sonographer. NCT02800031.

ObjectivesThe ability of contrast-enhanced MRI to distinguish between malignant and benign ovarian masses is limited. The aim of this meta-analysis is to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) in differentiating malignant from benign ovarian masses.MethodsA comprehensive literature search was performed in several authoritative databases to identify relevant articles. The weighted mean difference (WMD) and corresponding 95% confidence interval (95% CI) were calculated. We also used subgroup analysis to analyze study heterogeneity, and evaluated publication bias.ResultsThe meta-analysis is based on 21 studies, which reported the findings for 731 malignant and 918 benign ovarian masses. There was no significant difference in apparent diffusion coefficient (ADC) values for DWI between benign and malignant lesions (WMD = 0.22, 95% CI = -0.02–0.47, p = 0.08). Subgroup analysis by benign tumor type revealed higher ADC values (or a trend toward higher values) for cysts, cystadenomas and other benign tumors compared to malignant masses (cyst: WMD = 0.54, 95% CI = -0.05–1.12, p = 0.07; cystadenoma: WMD = 0.73, 95% CI = 0.38–1.07, p < 0.0001; other benign tumor: WMD = 0.16, 95% CI = -0.13–0.46, p = 0.28). On the other hand, lower ADC values (or a trend toward lower values) were observed for endometrioma and teratoma compared to malignant masses (endometrioma: WMD = -0.09, 95% CI = -0.47–0.29, p = 0.64; teratoma: WMD = -0.49, 95% CI = -0.85–0.12, p = 0.009). Subgroup analysis by mass property revealed higher ADC values in cystic tumor types than in solid types for both benign and malignant tumors. Significant study heterogeneity was observed. There was no notable publication bias.ConclusionsQuantitative DWI is not a reliable diagnostic method for differentiation between benign and malignant ovarian masses. This knowledge is essential in avoiding misdiagnosis of ovarian masses.

Guideline No. 404: Initial Investigation and Management of Benign Ovarian Masses.

The aim of this retrospective study was to determine the prevalence of ovarian masses and calculate the diagnostic performance of the pattern recognition approach in ovarian pathology. A total of 1001 patients diagnosed with ovarian mass were included, of which 92.6% were diagnosed with ovarian pathology and the presence of a pathological result, while 7.4% of cases were diagnosed with functional ovarian cyst. The prevalence of ovarian malignancy was 15%. A specific ultrasound diagnosis was suggested in 62.9% of all cases, while sonographers did not explicitly provide a diagnosis in remaining cases. A subjective assessment showed 80.3% sensitivity (95% confidence interval (CI) 68.7–89.1) and 97.6% specificity (95% CI 96–98.6) in differentiating between benign and malignant ovarian masses. The sensitivity and specificity for the diagnosis of endometriotic cyst were 77.03% and 90.63% and 63.19% and 94.3% for mature cystic teratoma, respectively. In conclusion, assessment showed good performance in differentiating between benign and malignant ovarian mass and it was possible to diagnose several specific ovarian tumours. Impact Statement What is already known on this subject? Pattern recognition is an acceptable method for classifying ovarian mass, which exhibits specific morphological features on grey-scale ultrasonography, and can be used to predict nature and histological type. What do the results of this study add? Even in the hands of an expert examiner, there were a number of cases in which the diagnoses could not be specifically stated. Pattern recognition correctly classified 90.3% of ovarian masses as either benign or malignant and correctly provided specific histologic diagnoses after exclusion of unspecified diagnosis in 80.6% of all cases. The diagnostic performance of this approach was high in differentiating between benign and malignant ovarian mass and in diagnosing some specific ovarian pathologies. What are the implications of these findings for clinical practice and/or further research? A subjective assessment is simple and easy to use in clinical practice and has shown promising results in classifying benign and malignant ovarian mass. Moreover, it can also be used to make some specific diagnoses. However, specialised and experienced gynaecological ultrasound examiners are required to provide the most accurate diagnosis. Therefore, criteria to describe ultrasound features and convincing operators to make a definite diagnosis as often as possible should be encouraged. A prospective study to verify diagnostic performance of pattern recognition or comparing with other ultrasonographic diagnostic tools should be considered.

Introduction/BackgroundAdvanced hemostasis devices – primarily electrothermal bipolar vessel sealing devices and ultrasonic devices – are frequently used in vulvar cancer surgery but their effect on the perioperative outcomes of vulvar...

ObjectiveThe purpose of this meta-analysis was to provide evidence for using maximum uptake value (SUVmax) and apparent diffusion coefficient (ADC) to quantitatively differentiate benign and malignant ovarian or adnexal masses, and to indirectly compare their diagnostic performance.Material and MethodsThe association between SUVmax, ADC and ovarian or adnexal benign and malignant masses was searched in PubMed, Cochrane Library, and Embase databases until October 1, 2021. Two authors independently extracted the data. Studies included in the analysis were required to provide data for the construction of a 2 × 2 contingency table to evaluate the diagnostic performance of SUVmax or ADC in differentiating benign and malignant ovarian or adnexal masses. The quality of the enrolled studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) instrument, and the meta-analysis was conducted using Stata software version 14.0. Forest plots were generated according to the sensitivity and specificity of SUVmax and ADC, and meta-regression analysis was further used to assess heterogeneity between studies.ResultsA total of 14 studies were finally included in this meta-analysis by gradually excluding duplicate literatures, conference abstracts, guidelines, reviews, case reports, animal studies and so on. The pooled sensitivity and specificity of SUVmax for quantitative differentiation of benign and malignant ovarian or adnexal masses were 0.88 and 0.89, respectively, and the pooled sensitivity and specificity for ADC were 0.87 and 0.80, respectively.ConclusionQuantitative SUVmax and ADC values have good diagnostic performance in differentiating benign and malignant ovarian or adnexal masses, and SUVmax has higher accuracy than ADC. Future prospective studies with large sample sizes are needed for the analysis of the role of SUVmax and ADC in the differentiation of benign and malignant ovarian or adnexal masses.

To determine the gray-scale and Doppler sonographic features that best enable discrimination between malignant and benign ovarian masses and develop a scoring system for accurate diagnosis with these features. Gray-scale and Doppler sonographic features of 211 adnexal masses were correlated with the final diagnosis; the most discriminating features for malignancy were selected with stepwise logistic regression. Twenty-eight masses were malignant and 183 benign. All masses with a markedly hyperechoic solid component or no solid component were benign. For masses with a nonhyperechoic solid component, additional features that allowed statistically significant discrimination of benignity from malignancy were, in decreasing order of importance, (a) location of flow at conventional color Doppler imaging, (b) amount of free intraperitoneal fluid, and (c) presence and thickness of septations. A scoring formula that made use of values based on the logistic regression equation had an area under the receiver operating characteristic curve of 0.98 +/- 0.01. The cutoff score with the highest accuracy had a sensitivity of 93% and specificity of 93%. A solid component is the most statistically significant predictor of a malignant ovarian mass. A multiparameter scoring system that uses three gray-scale and one Doppler feature, developed by means of stepwise logistic regression, has high sensitivity and specificity for predicting malignancy.

Objective:To evaluate the diagnostic accuracy of multidetector 64-slice computed tomography (MDCT) in the diagnosis and differentiation of benign and malignant ovarian masses using histopathology and surgical findings as the gold standard.Material and methods:This study was conducted in Aga Khan University Hospital, Karachi, Pakistan. Data was reviewed retrospectively from 1 November 2008 to 12 December 2009. One hundred patients found to have ovarian masses on CT scan were included in the study. CT scan was performed in all these patients after administration of oral and IV contrast. Ovarian masses were classified as benign and malignant on scan findings. Imaging findings were compared with histopathologic results and surgical findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of MDCT were calculated.Results:MDCT was found to have 97% sensitivity, 91% specificity, and an accuracy of 96% in the differentiation of benign and malignant ovarian masses, while PPV and NPV were 97% and 91%, respectively.Conclusion:MDCT imaging offers a safe, accurate and noninvasive modality to differentiate between benign and malignant ovarian masses.

BackgroundPreoperative characterization of complex solid and cystic adnexal masses is crucial for informing patients about possible surgical strategies. Our study aims to determine the usefulness of apparent diffusion coefficients (ADC) for characterizing complex solid and cystic adnexal masses.MethodsOne-hundred and 91 patients underwent diffusion-weighted (DW) magnetic resonance (MR) imaging of 202 ovarian masses. The mean ADC value of the solid components was measured and assessed for each ovarian mass. Differences in ADC between ovarian masses were tested using the Student’s t-test. The receiver operating characteristic (ROC) was used to assess the ability of ADC to differentiate between benign and malignant complex adnexal masses.ResultsEighty-five patients were premenopausal, and 106 were postmenopausal. Seventy-four of the 202 ovarian masses were benign and 128 were malignant. There was a significant difference between the mean ADC values of benign and malignant ovarian masses (p < 0.05). However, there were no significant differences in ADC values between fibrothecomas, Brenner tumors and malignant ovarian masses. The ROC analysis indicated that a cutoff ADC value of 1.20 x10-3 mm2/s may be the optimal one for differentiating between benign and malignant tumors.ConclusionsA high signal intensity within the solid component on T2WI was less frequently in benign than in malignant adnexal masses. The combination of DW imaging with ADC value measurements and T2-weighted signal characteristics of solid components is useful for differentiating between benign and malignant ovarian masses.