Management of adnexal mass: A comparison of five national guidelines

The Roles of Ovarian-Adnexal Reporting and Data System US and Ovarian-Adnexal Reporting and Data System MRI in the Evaluation of Adnexal Lesions

The Ovarian-Adnexal Reporting and Data System (O-RADS) is an evidence-based clinical support system for ovarian and adnexal lesion assessment in women of average risk. The system has both US and MRI components with separate but complementary lexicons and assessment categories to assign the risk of malignancy. US is an appropriate initial imaging modality, and O-RADS US can accurately help to characterize most adnexal lesions. MRI is a valuable adjunct imaging tool to US, and O-RADS MRI can help to both confirm a benign diagnosis and accurately stratify lesions that are at risk for malignancy. This article will review the O-RADS US and MRI systems, highlight their similarities and differences, and provide an overview of the interplay between the systems. When used together, the O-RADS US and MRI systems can help to accurately diagnose benign lesions, assess the risk of malignancy in lesions suspicious for malignancy, and triage patients for optimal management.

ACR Appropriateness Criteria® Clinically Suspected Adnexal Mass, No Acute Symptoms: 2023 Update

Asymptomatic adnexal masses are commonly encountered in daily radiology practice. Although the vast majority of these masses are benign, a small subset have a risk of malignancy, which require gynecologic oncology referral for best treatment outcomes. Ultrasound, using a combination of both transabdominal, transvaginal, and duplex Doppler technique can accurately characterize the majority of these lesions. MRI with and without contrast is a useful complementary modality that can help characterize indeterminate lesions and assess the risk of malignancy is those that are suspicious. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ReClassification of Patients with Ambiguous CA125 for Optimised Pre-Surgical Triage

Pre-surgical clinical assessment of an adnexal mass is a complex process, and ideally requires accurate and rapid identification of disease status. Gold standard biomarker CA125 is extensively used off-label for this purpose; however its performance is typically inadequate, particularly for the detection of early stage disease and discrimination between benign versus malignant status. We recently described a multi-marker panel (MMP) and associated risk index for the differentiation of benign from malignant ovarian disease. In this study we applied a net reclassification approach to assess the use of MMP index to rescue those cases where low CA125 incorrectly excludes cancer diagnoses, or where benign disease is incorrectly assessed as "high risk" due to elevated CA125. Reclassification of such patients is of significant value to assist in the timely and accurate referral for patients where CA125 titer is uninformative.

An algorithm for the pre-operative differentiation of benign ovarian tumours based on magnetic resonance imaging interpretation in a regional core hospital: A retrospective study

Objective

For selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors' 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals.

Study design

The medical records of 901 patients aged < 50 years with OTs from 1 January 2015-31 March 31 2023 were reviewed. First, the accuracy of pre-operative differentiation between Be-OTs and borderline/malignant ovarian tumours (Bo/Ma-OTs) was compared in each type of OT. Second, to identify the factors influencing differentiation between Be-OTs and Bo/Ma-OTs in 164 serous/mucinous ovarian tumours (SM-OTs), a multi-variate logistic regression analysis was performed to assess the effect of 13 factors, including MRI findings, OT size and tumour markers.

Results

In the comparison of diagnostic accuracy of pre-operative MRI for each OT type, accuracy was found to be notably high for ovarian endometrial cyst (OEC) (n = 409), ovarian mature cystic teratoma (OMCT) (n = 308), ovarian endometrioid adenocarcinoma (OEA) (n = 6) and ovarian clear cell adenocarcinoma (OCCA) (n = 14). On the other hand, discrepancies between MRI and pathological findings often occurred in SM-OTs, including ovarian serous cystadenoma (n = 86), ovarian mucinous adenocarcinoma (n = 61), ovarian serous adenocarcinoma (n = 12) and ovarian mucinous adenocarcinoma (n = 5). In the multi-variate logistic regression analysis of the latter 164 patients, in addition to MRI findings, OT size and carbohydrate antigen 125 also had an effect to some extent. The combination of MRI interpretation and OT size may enhance differentiation of Be-OTs and Bo/Ma-OTs.

Conclusions

Among four types of OTs (OEC, OMCT, OEA and OCCA), MRI interpretation was able to differentiate between Be-OTs and Bo/Ma-OTs almost perfectly. Additionally, to mitigate the difficulty in differentiating SM-OTs, OT size may be useful in combination with MRI findings, although further accumulation and analysis of OT cases is needed.

Current uptake and barriers to wider use of the International Ovarian Tumor Analysis (IOTA) models in Dutch gynaecological practice

OBJECTIVE

Correct referral of women with an ovarian tumor to an oncology department remains challenging. The International Ovarian Tumor Analysis (IOTA) consortium has developed models with higher diagnostic accuracy than the alternative Risk of Malignancy Index (RMI). This study explores the uptake of the IOTA models in Dutch hospitals and factors that impede or promote implementation. Optimal implementation is crucial to improve pre-operative classification of ovarian tumors, which may lead to better patient referral to the appropriate level of care.

STUDY DESIGN

In February 2021, an electronic questionnaire consisting of 37 questions was sent to all 72 hospitals in the Netherlands. One pre-selected gynaecologist per hospital was asked to respond on behalf of the department.

RESULTS

The study had a response rate of 93% (67/72 hospitals). All respondents (100%) were familiar with the IOTA models with 94% using them in practice. The logistic regression 2 (LR2)-model, Simple ultrasound-based rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model were used in respectively 40%, 67% and 73% of these hospitals. Respondents rated the models overall with an 8.2 (SD 1.8), 8.3 (SD 1.6) and 8.9 (SD 1.3) respectively for LR2, SR and ADNEX on a scale from 1 to 10. Moreover, 89% indicated to have confidence in the results of the IOTA models. The most important factors to improve further implementation are more training (43%), research on sensitivity, specificity and cost-effectiveness in the Dutch health care system (27%), easier usability (24%) and more consultation time (19%).

CONCLUSION

The IOTA ultrasound models are adopted in the majority of Dutch hospitals with the ADNEX model being used the most. While Dutch gynecologists have a strong familiarity and confidence in the models, the uptake varies in reality. Areas that warrant improvement in the Dutch context are more uniformity, education and more research. These findings can be helpful for other countries considering adopting the IOTA models.

Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images

Objective

This study aimed to evaluate and validate the performance of deep convolutional neural networks when discriminating different histologic types of ovarian tumor in ultrasound (US) images.

Material and methods

Our retrospective study took 1142 US images from 328 patients from January 2019 to June 2021. Two tasks were proposed based on US images. Task 1 was to classify benign and high-grade serous carcinoma in original ovarian tumor US images, in which benign ovarian tumor was divided into six classes: mature cystic teratoma, endometriotic cyst, serous cystadenoma, granulosa-theca cell tumor, mucinous cystadenoma and simple cyst. The US images in task 2 were segmented. Deep convolutional neural networks (DCNN) were applied to classify different types of ovarian tumors in detail. We used transfer learning on six pre-trained DCNNs: VGG16, GoogleNet, ResNet34, ResNext50, DensNet121 and DensNet201. Several metrics were adopted to assess the model performance: accuracy, sensitivity, specificity, FI-score and the area under the receiver operating characteristic curve (AUC).

Results

The DCNN performed better in labeled US images than in original US images. The best predictive performance came from the ResNext50 model. The model had an overall accuracy of 0.952 for in directly classifying the seven histologic types of ovarian tumors. It achieved a sensitivity of 90% and a specificity of 99.2% for high-grade serous carcinoma, and a sensitivity of over 90% and a specificity of over 95% in most benign pathological categories.

Conclusion

DCNN is a promising technique for classifying different histologic types of ovarian tumors in US images, and provide valuable computer-aided information.

The Discrepancy between Preoperative Tumor Markers and Imaging Outcomes in Predicting Ovarian Malignancy

Preoperative tumor markers and imaging often differ in predicting whether an ovarian tumor is malignant. Therefore, we evaluated the correlation between the predictive values of imaging and tumor markers for diagnosing ovarian tumors, especially when there were discrepancies between the two. We enrolled 1047 patients with ovarian tumors. The predictive values and concordance rates between the preoperative risk of ovarian malignancy algorithm (ROMA) and imaging, including CT and MRI, were evaluated. Diagnoses of 561 CT (77.9%) and 322 MRI group (69.2%) participants were consistent with the ROMA. Among them, 96.4% of the CT (541/561) and 92.5% of the MRI (298/322) group predicted an accurate diagnosis. In contrast, 67.3% (101/150) of CT and 75.2% (100/133) of MRI cases accurately predicted the diagnosis in cases with discrepancies between ROMA and CT or MRI; a total of 32% (48/150) of the CT and 25.5% (34/133) of the MRI group showed an accurate ROMA diagnosis in cases with discrepancies between ROMA and imaging. In the event of a discrepancy between ROMA and imaging when ovarian tumor malignancy prediction, the question is which method should take precedence. This study demonstrates that MRI has the greatest diagnostic accuracy, followed by CT and ROMA. It is also important to understand underlying diseases and benign conditions and rare histopathologies of malignant tumors.

O-RADS MRI: A Systematic Review and Meta-Analysis of Diagnostic Performance and Category-wise Malignancy Rates

Background US-indeterminate adnexal lesions remain an important indication for gynecologic surgery. MRI can serve as a problem-solving tool through the use of the Ovarian-Adnexal Imaging Reporting and Data System (O-RADS) MRI lexicon, which is based on the ADNEX MR scoring system. Purpose To perform a systematic review and meta-analysis of the diagnostic performance of pelvic MRI interpreted using the ADNEX or O-RADS MRI stratification systems to characterize US-indeterminate adnexal lesions and of the category-wise malignancy rates. Materials and Methods A systematic literature search from May 2013 (publication of the ADNEX MR score) to September 2022 was performed. Studies reporting the use of pelvic MRI interpreted with the ADNEX or O-RADS MRI systems to characterize US-indeterminate adnexal lesions, with pathologic examination and/or follow-up as the reference standard, were included. Summary estimates of diagnostic performance were obtained with the bivariate random-effects model, while category-wise summary malignancy rates of O-RADS MRI 2, 3, 4, and 5 lesions were obtained with a random-effects model. Effects of covariates on heterogeneity and diagnostic performance were investigated through meta-regression. Results Thirteen study parts from 12 studies (3731 women, 4520 adnexal lesions) met the inclusion criteria. Diagnostic performance meta-analysis for 4012 lesions found a 92% summary sensitivity (95% CI: 88, 95) and a 91% summary specificity (95% CI: 89, 93). The meta-analysis of malignancy rates for 3641 lesions showed summary malignancy rates of 0.1% (95% CI: 0, 1) among O-RADS MRI 2 lesions, 6% (95% CI: 3, 9) among O-RADS MRI 3 lesions, 60% (95% CI: 52, 67) among O-RADS MRI 4 lesions, and 96% (95% CI: 92, 99) among O-RADS MRI 5 lesions. Conclusion Pelvic MRI interpreted with the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI lexicon had high diagnostic performance for the characterization of US-indeterminate adnexal lesions. Summary estimates of malignancy rates in the O-RADS MRI 4 and O-RADS MRI 5 categories were higher than predicted ones. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Lee and Kang in this issue.

BRAK and APRIL as novel biomarkers for ovarian tumors

Aims: To evaluate BRAK and APRIL in serum samples from healthy patients and an ovarian tumor group and analyze their effective value as biomarkers. Materials & methods: BRAK and APRIL were measured in 197 serum samples including 34 healthy controls, 48 patients with benign ovarian cysts and 115 patients with ovarian cancer, and the best statistical cutoff values were calculated. Then, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value for selected cutoff points were assessed. Results: The healthy control group had statistically significant higher BRAK and lower APRIL than the ovarian tumor group. BRAK was excellent for differentiating healthy patients from patients with ovarian tumors, showing area under the receiver operating characteristic curve 0.983, 98.16% sensitivity and 100% specificity. When BRAK was combined with APRIL and CA-125, it also played a role in distinguishing benign cysts from malignancies with area under the curve 0.864, 81.74% sensitivity and 79.17% specificity. Conclusions: BRAK and APRIL are good candidates for ovarian tumor biomarkers.