After the introduction of the Ovarian-Adnexal Reporting and Data System (O-RADS) for magnetic resonance imaging (MRI), several studies with diverse characteristics have been published to assess its diagnostic performance. This systematic review and meta-analysis aimed to assess the diagnostic performance of O-RADS MRI scoring for adnexal masses, accounting for the risk of selection bias.

The PubMed, Scopus, Web of Science, and Cochrane databases were searched for eligible studies. Borderline or malignant lesions were considered malignant. All O-RADS MRI scores ≥4 were considered positive. The quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The pooled sensitivity, specificity, and likelihood ratio (LR) values were calculated, considering the risk of selection bias.

Fifteen eligible studies were found, and five of them had a high risk of selection bias. Between-study heterogeneity was low-to-moderate for sensitivity but substantial for specificity (I2 values were 35.5% and 64.7%, respectively). The pooled sensitivity was significantly lower in the studies with a low risk of bias compared with those with a high risk of bias (93.0% and 97.5%, respectively; P = 0.043), whereas the pooled specificity was not different (90.4% for the overall population). The negative and positive LRs were 0.08 [95% confidence interval (CI) 0.05–0.11] and 10.0 (95% CI 7.7–12.9), respectively, for the studies with low risk of bias and 0.03 (95% CI 0.01–0.10) and 10.3 (95% CI 3.8–28.3), respectively, for those with high risk of bias.

The overall diagnostic performance of the O-RADS system is very high, particularly for ruling out borderline/malignant lesions, but with a moderate ruling-in potential. Studies with a high risk of selection bias lead to an overestimation of sensitivity.

The O-RADS system demonstrates considerable diagnostic performance, particularly in ruling out borderline or malignant lesions, and should routinely be used in practice. The high between-study heterogeneity observed for specificity suggests the need for improvement in the consistent characterization of the benign lesions to reduce false positive rates.

Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automatic assignment of O-RADS scores from reports could increase adoption and accuracy. Purpose To evaluate the accuracy of large language models (LLMs), after strategic optimization, for automatically calculating O-RADS scores from reports. Materials and Methods This retrospective single-center study from a large quaternary care cancer center included consecutive gadolinium chelate-enhanced pelvic MRI reports with at least one assigned O-RADS score from July 2021 to October 2023. Reports from January 2018 to October 2019 (before O-RADS MRI implementation) were randomly selected for additional testing. Reference standard O-RADS scores were determined by radiologists interpreting reports. After prompt optimization using a subset of reports, two LLM-based strategies were evaluated: few-shot learning with GPT-4 (version 0613; OpenAI) prompted with O-RADS rules ("LLM only") and a hybrid strategy leveraging GPT-4 to classify features fed into a deterministic formula ("hybrid"). Accuracy of each model and originally reported scores were calculated and compared using the McNemar test. Results A total of 284 reports from 284 female patients (mean age, 53.2 years ± 16.3 [SD]) with 372 adnexal lesions were included: 10 reports in the training set (16 lesions), 134 reports in the internal test set 1 (173 lesions; 158 O-RADS assigned), and 140 reports in internal test set 2 (183 lesions). For assigning O-RADS MRI scores, the hybrid model accuracy (97%; 168 of 173) outperformed LLM-only model (90%; 155 of 173; P = .006). For lesions with an originally reported O-RADS score, hybrid model accuracy exceeded that of reporting radiologists (97% [153 of 158] vs 88% [139 of 158]; P = .004). Hybrid model also outperformed LLM-only model for 183 lesions from before O-RADS implementation (95% [173 of 183] vs 87% [159 of 183], respectively; P = .01). Conclusion A hybrid LLM-based application, combining LLM feature classification with deterministic elements, accurately assigned O-RADS MRI scores from report descriptions, exceeding both an LLM-only strategy and the original reporting radiologist. © RSNA, 2025 Supplemental material is available for this article.

To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning.

Dynamic contrast-enhanced pelvic MRI examinations of 471 lesions were retrospectively analysed and assessed by 3 radiologists according to O-RADS MRI criteria. Radiomic data were extracted from T2 and post-contrast fat-suppressed T1-weighted images. Using these data, an artificial neural network (ANN), support vector machine, random forest, and naive Bayes models were constructed.

Among all readers, the lowest agreement was found for the O-RADS 4 group (kappa: 0.669; 95% confidence interval [CI] 0.634-0.733), followed by the O-RADS 5 group (kappa: 0.709; 95% CI 0.678-0.754). O-RADS 4 predicted a malignancy with an area under the curve (AUC) value of 74.3% (95% CI 0.701-0.782), and O-RADS 5 with an AUC of 95.5% (95% CI 0.932-0.972) (P < .001). Among the machine learning models, ANN achieved the highest success, distinguishing O-RADS groups with an AUC of 0.948, a precision of 0.861, and a recall of 0.824.

The interobserver agreement and diagnostic sensitivity of the O-RADS MRI in assigning O-RADS 4-5 were not perfect, indicating a need for structural improvement. Integrating artificial intelligence into MRI protocols may enhance their performance.

Machine learning can achieve high accuracy in the correct classification of O-RADS MRI. Malignancy prediction rates were 74% for O-RADS 4 and 95% for O-RADS 5.

To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS).

A total of 663 ultrasound images of adnexal mass were collected and divided into two sets according to experienced radiologists: a low malignant risk set (n = 446) and an intermediate-high malignant risk set (n = 217). Deep learning segmentation models were trained and selected to automatically segment adnexal masses. Radiomics features were extracted utilizing a feature analysis system in Pyradiomics. Feature selection was conducted using the Spearman correlation analysis, Mann-Whitney U-test, and least absolute shrinkage and selection operator (LASSO) regression. A nomogram integrating radiomic and clinical features using a machine learning model was established and evaluated. The SHapley Additive exPlanations were used for model interpretability and visualization.

The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925-0.996). The nomogram using the LightGBM algorithm reached the best AUC (0.966, 95% CI: 0.927-1.000). The diagnostic performance of the nomogram was comparable to that of experienced radiologists (p > 0.05) and outperformed that of less-experienced radiologists (p < 0.05). The model significantly improved the diagnostic accuracy of less-experienced radiologists.

The segmentation model serves as a valuable tool for the automated delineation of adnexal lesions. The machine learning model exhibited commendable classification capability and outperformed the diagnostic performance of less-experienced radiologists.

The ultrasound radiomics-based machine learning model holds the potential to elevate the professional ability of less-experienced radiologists and can be used to assist in the clinical screening of ovarian cancer.

We developed an image segmentation model to automatically delineate adnexal masses. We developed a model to classify adnexal masses based on O-RADS. The machine learning model has achieved commendable classification performance. The machine learning model possesses the capability to enhance the proficiency of less-experienced radiologists. We used SHapley Additive exPlanations to interpret and visualize the model.

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.

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.

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.

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.

The aim of this study was to compare the diagnostic performance of the Gynecology Imaging Reporting and Data System (GI-RADS) and Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) classification systems and assess their capacity to stratify the risk of malignancy in adnexal masses (AMs).

A comprehensive search of MEDLINE (PubMed), Scopus, Web of Science, and Google Scholar was conducted to identify articles published between January 2020 and August 2023. The quality of the studies, the risk of bias, and concerns regarding applicability were assessed using QUADAS-2.

The search yielded 132 citations. Five articles, which included a total of 2,448 AMs, were ultimately selected for inclusion. The risk of bias was high in all articles regarding patient selection, low in four studies for the index test, and unclear in three papers for the reference test. For GI-RADS, the pooled sensitivity and specificity were 90.8% (95% confidence interval [CI], 86.0% to 94.0%) and 91.5% (95% CI, 89.0% to 93.0%), respectively. For O-RADS, the pooled sensitivity and specificity were 95.1% (95% CI, 93.0% to 97.0%) and 88.8% (95% CI, 85.0% to 92.0%), respectively. O-RADS demonstrated greater sensitivity for malignancy than GI-RADS (P<0.05). Heterogeneity was moderate for both sensitivity and specificity with respect to GIRADS; for O-RADS, heterogeneity was moderate for sensitivity and high for specificity.

Both GI-RADS and O-RADS US demonstrate good diagnostic performance in the preoperative assessment of AMs. However, the O-RADS classification provides superior sensitivity.

The purpose of this study was to evaluate the contribution of apparent diffusion coefficient (ADC) analysis of the solid tissue of adnexal masses to optimize tumor characterization and possibly refine the risk stratification of the O-RADS MRI 4 category.

The EURAD cohort was retrospectively analyzed to select all patients with an adnexal mass with solid tissue and feasible ADC measurements. Two radiologists independently measured the ADC values of solid tissue, excluding necrotic areas, surrounding structures, and magnetic susceptibility artifacts. Significant differences in diffusion quantitative parameters in the overall population and according to the morphological aspect of solid tissue were analyzed to identify its impact on ADC reliability. Receiver operating characteristics curve (ROC) was used to determine the optimum cutoff of the ADC for distinguishing invasive from non-invasive tumors in the O-RADS MRI score 4 population.

The final study population included 180 women with a mean age of 57 ± 15.5 (standard deviation) years; age range: 19-95 years) with 93 benign, 23 borderline, and 137 malignant masses. The median ADC values of solid tissue was greater in borderline masses (1.310 × 10-3 mm2/s (Q1, Q3: 1.152, 1.560 × 10-3 mm2/s) than in benign masses (1.035 × 10-3 mm2/s; Q1, Q3: 0.900, 1.560 × 10-3 mm2/s) (P= 0.002) and in benign tumors compared by comparison with invasive masses (0.850 × 10-3 mm2/s; Q1, Q3: 0.750, 0.990 × 10-3 mm2/s) (P < 0.001). Solid tissue corresponded to irregular septa or papillary projection in 18.6% (47/253), to a mural nodule or a mixed mass in 46.2% (117/253), and to a purely solid mass in 35.2% (89/253) of adnexal masses. In mixed masses or masses with mural nodule subgroup, invasive masses had a significantly lower ADC (0.830 × 10-3 mm2/s (Q1, Q3: 0.738, 0.960) than borderline (1.385; Q1, Q3: 1.300, 1.930) (P= 0.0012) and benign masses (P= 0.04). An ADC cutoff of 1.08 × 10-3 mm2/s yielded 71.4% sensitivity and 100% specificity for identifying invasive lesions in the mixed or mural nodule subgroup with an AUC of 0.92 (95% confidence interval: 0.76-0.99).

ADC analysis of solid tissue of adnexal masses could help distinguish invasive masses within the O-RADS MRI 4 category, especially in mixed masses or those with mural nodule.

Background The Ovarian-Adnexal Imaging Reporting and Data System (O-RADS) US risk score can be used to accurately stratify ovarian lesions based on morphologic characteristics. However, there are no large multicenter studies assessing the potential impact of using O-RADS US version 2022 risk score in patients referred for surgery for an ovarian or adnexal lesion. Purpose To retrospectively determine the proportion of patients with ovarian or adnexal lesions without acute symptoms who may have been managed conservatively by using the O-RADS US version 2022 risk score. Materials and Methods This multicenter retrospective study included patients with ovarian cystic lesions and nonacute symptoms who underwent surgical resection after US before the introduction of O-RADS US between January 2011 and December 2014. Investigators blinded to the final diagnoses recorded lesion imaging features and O-RADS US risk scores. The frequency of malignancy and the diagnostic performance of the risk score were calculated. The Mann-Whitney test and Fisher exact test were performed, with P < .05 indicating a statistically significant difference. Results A total of 377 patients with surgically resected lesions were included. Among the resected lesions, 42% (157 of 377) were assigned an O-RADS US risk score of 2. Of the O-RADS US 2 lesions, 54% (86 of 157) were nonneoplastic, 45% (70 of 157) were dermoids or other benign tumors, and less than 1% (one of 157) were malignant. Using O-RADS US 4 as the optimal threshold for malignancy prediction yielded a 94% (68 of 72) sensitivity, 64% (195 of 305) specificity, 38% (68 of 178) positive predictive value, and 98% (195 of 199) negative predictive value. Conclusion In patients without acute symptoms who underwent surgery for ovarian and adnexal lesions before the O-RADS US risk score was published, nearly half (42%) of surgically resected lesions retrospectively met the O-RADS US 2 version 2022 criteria. In these patients, imaging follow-up or conservative management could have been offered. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Fournier in this issue.

This study aimed to apply the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR), the Ovarian-Adnexal Reporting and Data System (O-RADS) and contrast-enhanced ultrasound (CEUS) in an identical cohort of Chinese patients and to analyze their performance in discrimination of ovarian masses with solid components.

This was a two-center retrospective study that included a total of 94 ovarian lesions in 86 women enrolled from January 2018 to February 2023. The lesions were classified by using the IOTA terminology and CEUS was performed for the lesions exhibiting solid components on ultrasonography, IOTA SR and O-RADS were applied, and CEUS images were analyzed retrospectively. We assessed the time to wash-in, time to peak intensity (PI), PI compared to myometrium, and time to wash-out, and observed statistically significant differences between benign and malignant lesions in the first three parameters. CEUS characteristics were employed to determine CEUS scores for benign (score 0) and malignant (score 3) lesions. Subsequently, the lesions were reassessed based on the IOTA SR and O-RADS classifications and CEUS scores. The sensitivity, specificity, and area under the receiver-operating-characteristics curve (AUC) of the different models were also determined.

Among the 94 ovarian lesions, 46 (48.9%) were benign and 48 (51.1%) were malignant. It was found that in the 60 lesions to which the SR could be applied, the sensitivity, specificity, and AUC was 0.900, 0.667, and 0.783, respectively. The sensitivity, specificity, and AUC of O-RADS was observed to be 1.000, 0.283 and 0.641, respectively. When SR and O-RADS were combined with CEUS, their sensitivity, specificity, and AUC values were increased to 0.917, 0.891, 0.904, and 0.958, 0.783, 0.871, respectively.

IOTA SR and O-RADS exhibited relatively low specificity in differentiating malignant from benign ovarian lesions with the solid components, and their diagnostic performance can be significantly improved when combined with CEUS.

To evaluate the additional advantages of integrating contrast-enhanced ultrasound (CEUS) into the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) for the characterization of adnexal lesions with solid components.

This prospective multicenter study recruited women suspected of having adnexal lesions with solid components between September 2021 and December 2022. All patients scheduled for surgery underwent preoperative CEUS and US examinations. The lesions were categorized according to the O-RADS US system, and quantitative CEUS indexes were recorded. Pathological results served as the reference standard. Univariable and multivariable analyses were performed to identify risk factors for malignancy in adnexal lesions with solid components. Receiver operating characteristic (ROC) curve analysis was employed to assess diagnostic performance.

A total of 180 lesions in 175 women were included in the study. Among these masses, 80 were malignant and 100 were benign. Multivariable analysis revealed that serum CA-125, the presence of acoustic shadowing, and peak intensity (PI) ratio (PImass/PIuterus) of solid components on CEUS were independently associated with adnexal malignancy. The modified CEUS risk stratification model demonstrated superior diagnostic value in assessing adnexal lesions with solid components compared to O-RADS US (AUC: 0.91 vs 0.78, p < 0.001) and exhibited comparable performance to the Assessment of Different NEoplasias in the adnexa (ADNEX) model (AUC 0.91 vs 0.86, p = 0.07).

Our findings underscore the potential value of CEUS as an adjunctive tool for enhancing the precision of diagnostic evaluations of O-RADS US.

The promising performance of the modified CEUS risk stratification model suggests its potential to mitigate unnecessary surgeries in the characterization of adnexal lesions with solid components.

• The additional value of CEUS to O-RADS US in distinguishing between benign and malignant adnexal lesions with solid components requires further evaluation. • The modified CEUS risk stratification model displayed superior diagnostic value and specificity in characterizing adnexal lesions with solid components when compared to O-RADS US. • The inclusion of CEUS demonstrated potential in reducing the need for unnecessary surgeries in the characterization of adnexal lesions with solid components.

Endometriosis, an inflammatory disease primarily affecting the pelvis and peritoneum, manifests with pelvic pain, dysmenorrhea, dyschezia, dyspareunia, and infertility. Despite its ubiquity, the management of endometriosis is challenging due to its heterogeneous presentation, limitations in diagnostic methods, variable therapeutic responses, and personal and socio-cultural impact on quality of life. This review attempts to consolidate the current literature on endometriosis occurring during and beyond menopause, and to present details regarding management strategies that take into account individual outcomes and goals when managing this condition. The topics included in this review are the clinical features and differential diagnosis of pelvic pain in postmenopausal patients, imaging considerations, serum and laboratory biomarkers, indications for surgery, the principles of hormone replacement therapy, the de novo development of endometriosis after menopause, and malignant transformation. Each topic includes a summary of the current literature, utilizing clinical research, case reports, and expert opinion. Despite a better understanding of the impact of endometriosis beyond menopause, there are many limitations to this condition, specifically with regard to cancer risk and indications for surgery. The existing evidence supports the use of shared decision making and the incorporation of patient preferences in guiding clinical management. Future research endeavors must shed light on the natural history of postmenopausal endometriosis through longitudinal studies in order to foster a deeper understanding of its complicated disease course across women's lifespans.

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.

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment's (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.

Pre-surgical clinical assessment of an adnexal mass typically relies on transvaginal ultrasound for comprehensive morphological assessment, with further support provided by biomarker measurements and clinical evaluation. Whilst effective for masses that are obviously benign or malignant, a large proportion of masses remain sonographically indeterminate at surgical referral. As a consequence, post-surgical diagnoses of benign disease can outnumber malignancies up to 9-fold, while less than 50% of cancer cases receive a primary referral to a gynecological oncology specialist. We recently described a blood biomarker signature (multi-marker panel-MMP) that differentiated patients with benign from malignant ovarian disease with high accuracy. In this study, we have examined the use of the MMP, both individually and in combination with transvaginal ultrasound, as an alternative tool to CA-125 for enhanced decision making in the pre-surgical referral process.

Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS).

The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction.

The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful.

This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.

This study aims to systematically compare the diagnostic performance of the Ovarian-Adnexal Reporting and Data System with the International Ovarian Tumor Analysis Simple Rules and the Assessment of Different NEoplasias in the adneXa model for risk stratification of ovarian cancer and adnexal masses.

A literature search of online databases for relevant studies up to July 2023 was conducted by two independent reviewers. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic model. The quality of the included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Metaregression and subgroup analyses were performed to explore the impact of varying clinical settings.

A total of 13 studies met the inclusion criteria. The pooled sensitivity and specificity for eight head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model were 0.96 (95% CI 0.92-0.98) and 0.82 (95% CI 0.71-0.90) vs. 0.94 (95% CI 0.91-0.95) and 0.83 (95% CI 0.77-0.88), respectively, and for seven head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the International Ovarian Tumor Analysis Simple Rules, the pooled sensitivity and specificity were 0.95 (95% CI 0.93-0.97) and 0.75 (95% CI 0.62-0.85) vs. 0.91 (95% CI 0.82-0.96) and 0.86 (95% CI 0.76-0.93), respectively. No significant differences were found between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model as well as the International Ovarian Tumor Analysis Simple Rules in terms of sensitivity (P = 0.57 and P = 0.21) and specificity (P = 0.87 and P = 0.12). Substantial heterogeneity was observed among the studies for all three guidelines.

All three guidelines demonstrated high diagnostic performance, and no significant differences in terms of sensitivity or specificity were observed between the three guidelines.

To evaluate the efficacy of the O-RADS MRI criteria in the stratification of risk of malignancy of solid or sonographically indeterminate ovarian masses and assess the interobserver agreement of this classification between experienced and inexperienced radiologists.

This single-centre retrospective study included patients from 2019 to 2022 with sonographically indeterminate or solid ovarian masses who underwent MRI with a specific protocol for characterisation according to O-RADS MRI specifications. Each study was evaluated using O-RADS lexicon by two radiologists, one with 17 years of experience in gynaecological radiology and another with 4 years of experience in general radiology. Findings were classified as benign, borderline, or malignant according to histology or stability over time. Diagnostic performance and interobserver agreement were assessed.

A total of 183 patients with US indeterminate or solid adnexal masses were included. Fifty-seven (31%) did not have ovarian masses, classified as O-RADS 1. The diagnostic performance for scores 2-5 was excellent with a sensitivity, specificity, PPV, and NPV of 97.4%, 100%, 96.2%, and 100%, respectively by the experienced radiologist and 96.1%, 92.0%, 93.9%, and 94.8% by the inexperienced radiologist. Interobserver concordance was very high (Kappa index 0.92). Almost all the misclassified cases were due to misinterpretation of the classification similar to reports in the literature.

The diagnostic performance of O-RADS MRI determined by either experienced or inexperienced radiologists is excellent, facilitating decision-making with high diagnostic accuracy and high reproducibility. Knowledge of this classification and use of assessment tools could avoid frequent errors due to misinterpretation.

Up to 31% of ovarian masses are considered indeterminate by transvaginal US and 32% of solid lesions considered malignant by transvaginal US are benign. The O-RADs MRI accurately classifies these masses, even when used by inexperienced radiologists, thereby avoiding incorrect surgical approaches.

• O-RADS MRI accurately classifies indeterminate and solid ovarian masses by ultrasound. • There is excellent interobserver agreement between experienced and non-experienced radiologists. • O-RADS MRI is a helpful tool to assess clinical decision-making in ovarian tumours.

This manuscript is a collaborative, multi-institutional effort by members of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease Focus Panel and the European Society of Urogenital Radiology Women Pelvic Imaging working group. The manuscript reviews the key role radiologists play at tumor board and highlights key imaging findings that guide management decisions in patients with the most common gynecologic malignancies including ovarian cancer, cervical cancer, and endometrial cancer.

First published in 2019, the Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, enables stratification of these lesions with use of a numeric score based on morphologic features to indicate the risk of malignancy, and offers management guidance. This risk stratification system has subsequently been validated in retrospective studies and has yielded good interreader concordance, even with users of different levels of expertise. As use of the system increased, it was recognized that an update was needed to address certain clinical challenges, clarify recommendations, and incorporate emerging data from validation studies. Additional morphologic features that favor benignity, such as the bilocular feature for cysts without solid components and shadowing for solid lesions with smooth contours, were added to O-RADS US for optimal risk-appropriate scoring. As O-RADS US 4 has been shown to be an appropriate cutoff for malignancy, it is now recommended that lower-risk O-RADS US 3 lesions be followed with US if not excised. For solid lesions and cystic lesions with solid components, further characterization with MRI is now emphasized as a supplemental evaluation method, as MRI may provide higher specificity. This statement summarizes the updates to the governing concepts, lexicon terminology and assessment categories, and management recommendations found in the 2022 version of O-RADS US.

Background Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized method with which to stratify lesions into risk of malignancy categories, which is crucial for proper management. Purpose To perform a systematic review and meta-analysis to estimate malignancy rates for each O-RADS US score and evaluate the diagnostic performance of combined O-RADS US scores 4 and 5 in the diagnosis of malignancy. Materials and Methods A systematic literature search from the inception of the MEDLINE, EMBASE, and Web of Science databases through January 27, 2023, was performed for articles that reported using the O-RADS US stratification system and included malignancy rates per each O-RADS score. Bivariate random-effects models were used to determine the pooled malignancy rates for each O-RADS US score and to obtain summary estimates of the diagnostic performance of combined O-RADS US scores 4 and 5 in the diagnosis of malignant lesions. Results The final analysis included 18 studies consisting of 11 605 patients and 11 818 ovarian-adnexal lesions, with 2996 malignant (25.4%) and 8822 benign (74.6%) lesions. No malignant lesions were reported in O-RADS 1 category. The pooled percentages of malignancy were 0.6% (95% CI: 0.3, 1.0) for O-RADS 2, 3.9% (95% CI: 2.5, 5.4) for O-RADS 3, 43.5% (95% CI: 33.8, 53.2) for O-RADS 4, and 87.3% (95% CI: 83.0, 91.7) for O-RADS 5. The pooled sensitivity and specificity of combined O-RADS scores 4 and 5 in the diagnosis of malignant lesions were 95.6% (95% CI: 94.0, 97.2) and 76.6% (95% CI: 70.4, 82.7), respectively. Conclusion Each O-RADS US score provided the intended probability of malignant lesions as outlined by the O-RADS risk stratification system. When O-RADS US scores 4 and 5 were combined as a predictor for malignancy, O-RADS US showed a high sensitivity and moderate specificity. Clinical trial registration no. CRD42022352166 © RSNA, 2023 Supplemental material is available for this article.

The O-RADS system is a new proposal for establishing the risk of malignancy of adnexal masses using ultrasound. The objective of this study is to assess the agreement and diagnostic performance of O-RADS when using the IOTA lexicon or ADNEX model for assigning the O-RADS risk group.

Retrospective analysis of prospectively collected data. All women diagnosed as having an adnexal mass underwent transvaginal/transabdominal ultrasound. Adnexal masses were classified according to the O-RADS classification, using the criterion of the IOTA lexicon and according to the risk of malignancy determined by the ADNEX model. The agreement between both methods for assigning the O-RADS group was estimated using weighted Kappa and the percentage of agreement. The sensitivity and specificity of both approaches were calculated.

454 adnexal masses in 412 women were evaluated during the study period. There were 64 malignant masses. The agreement between the two approaches was moderate (Kappa: 0.47), and the percentage of agreement was 46%. Most disagreements occurred for the groups O-RADS 2 and 3 and for groups O-RADS 3 and 4. The sensitivity and specificity for O-RADS using the IOTA lexicon and O-RADS using the ADNEX model were 92.2% and 86.1%, and 85.9% and 87.4%, respectively.

The diagnostic performance of O-RADS classification using the IOTA lexicon as opposed to the IOTA ADNEX model is similar. However, O-RADS group assignment varies significantly, depending on the use of the IOTA lexicon or the risk estimation using the ADNEX model. This fact might be clinically relevant and deserves further research.