Gynecologic imaging reporting and data system: a new proposal for classifying adnexal masses on the basis of sonographic findings

Diagnostic performance of ultrasound reporting systems in evaluation of adnexal masses: A prospective observational study

OBJECTIVE(S)

To evaluate and compare diagnostic performance of ultrasound-based reporting systems IOTA SR, ADNEX, GIRADS, ORADS for discrimination between benign and malignant adnexal masses.

STUDY DESIGN

A prospective observational study in a tertiary care hospital's Obstetrics and Gynaecology department evaluated pre-operative ultrasound imaging for adnexal masses in 80 cases, comparing various reporting systems' sensitivity and specificity against histopathology as gold standard using STATA version 17.0 for data analysis.

RESULTS

Among the 80 masses, 55 % (44/80) were confirmed as benign on histopathology, while 45 % were identified as malignant. The sensitivity and specificity of SR was 100 % (95 %CI: 90.0-100) and 97.1 % (95 %CI: 84.7-99.9) respectively. Eleven masses (13.8 %) were inconclusive, reducing specificity to 75 % (95 %CI:59.7-86.8).In ADNEX optimal cut-off for risk of malignancy was 34.1 % with sensitivity of 86.1 % (95 % CI: 70.5-95.3) and specificity of 90.9 % (95 % CI: 78.3-97.5). Considering GIRADS 4-5 and risk threshold of ≥10 % (ORADS 4-5) as predictors of malignancy sensitivity was 100 % (95 %CI: 90.3-100) and specificity was 70.5 % (95 %CI: 54.8-83.2) for GIRADS and ORADS. All reporting systems were comparable (p = 0.7). ADNEX identified 72.7 % (8/11) of inconclusive cases, outperforming GIRADS/ORADS which correctly classified 27.2 % (3/11) cases. When applied to misclassified GIRADS/ORADS 4-5 category, ADNEX demonstrated superior performance by correctly classifying 76.9 % (10/13) masses, while SR achieved correct classification in only 38.5 % (5/13) masses.

CONCLUSION(S)

All classification systems showed comparable accuracy in malignancy risk identification on imaging. GIRADS/ORADS tended to overestimate malignancy risk. The present study recommends a two-step strategy, leveraging higher specificity of ADNEX model for improved stratification of adnexal masses.

Clinical value of ACR O-RADS combined with CA125 in the risk stratification of adnexal masses

Purpose

To develop a combined diagnostic model integrating the subclassification of the 2022 version of the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) with carbohydrate antigen 125 (CA125) and to validate whether the combined model can offer superior diagnostic efficacy than O-RADS alone in assessing adnexal malignancy risk.

Methods

A retrospective analysis was performed on 593 patients with adnexal masses (AMs), and the pathological and clinical data were included. According to the large differences in malignancy risk indices for different image features in O-RADS category 4, the lesions were categorized into groups A and B. A new diagnostic criterion was developed. Lesions identified as category 1, 2, 3, or 4A with a CA125 level below 35 U/ml were classified as benign. Lesions identified as category 4A with a CA125 level more than or equal to 35 U/ml and lesions with a category of 4B and 5 were classified as malignant. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of O-RADS (v2022), CA125, and the combined model in the diagnosis of AMs were calculated and compared.

Results

The sensitivity, specificity, PPV, NPV, accuracy, and AUCs of the combined model were 92.4%, 96.5%, 80.2%, 98.8%, 94.1%, and 0.945, respectively. The specificity, PPV, accuracy, and AUC of the combined model were significantly higher than those of O-RADS alone (all P < 0.01). In addition, both models had acceptable sensitivity and NPV, but there were no significant differences among them (P > 0.05).

Conclusion

The combined model integrating O-RADS subclassification with CA125 could improve the specificity and PPV in diagnosing malignant AMs. It could be a valuable tool in the clinical application of risk stratification of AMs.

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.

Clinical Ultrasound Applications in Obstetrics and Gynecology in the Year 2024

Ultrasound imaging stands as a fundamental technology in the realms of obstetrics and gynecology, utilizing high-frequency sound waves to create detailed images of the internal structures of the body [...].

Evaluation of American College of Radiology Ovarian-Adnexal Reporting and Data System ultrasound to predict malignancy risk in adnexal lesions

AIMS

To validate the diagnostic performance of Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound for preoperative adnexal lesions in an external center. The secondary aim was to evaluate the performance of a strategy test including O-RADS ultrasound evaluation and subjective assessment of higher malignant risk lesions.

METHODS

One hundred thirty patients with 158 ovarian-adnexal lesions were enrolled in the study. Each lesion was assigned an O-RADS score after real-time ultrasound examination by one experienced radiologist. A second subjective assessment by an expert was performed for O-RADS 4 and O-RADS 5 lesions. The histopathological diagnosis was used as the reference standard.

RESULTS

A total of 126 benign and 32 malignant adnexal masses were included in the study. The area under the receiver operating characteristic curve of O-RADS ultrasound was 0.950, with a cutoff value > O-RADS 3. The sensitivity, specificity, and negative and positive predictive values were 100% (95% confidence interval [CI], 0.867-1), 83.3% (95% CI, 0.754-0.892), 60.4% (95% CI, 0.460-0.732), and 100% (95% CI, 0.956-1), respectively. For the strategy test, the sensitivity, specificity, negative and positive predictive values were 100% (95% CI, 0.867-1), 92.1% (95% CI, 0.855-0.959), 76.2% (95% CI, 0.602-0.874), and 100% (95% CI, 0.960-1), respectively. In comparison with O-RADS ultrasound, the specificity and negative predictive value of the strategy test were slightly higher (p < 0.05).

CONCLUSIONS

Good diagnostic performance of the O-RADS ultrasound in adnexal lesions can be achieved by experienced radiologists in clinical practice. A second subjective assessment of sonographic findings can be applied to O-RADS 4 and 5 lesions.

IOTA simple rules: An efficient tool for evaluation of ovarian tumors by non-experienced but trained examiners - A prospective study

Objectives

A simple and efficient tool for evaluating ovarian tumors in general hospitals where radiologists without experience in gynecological ultrasound is necessary. This study aims to evaluate the diagnostic performance of IOTA simple rules in initial classification of ovarian tumors by non-experienced examiners who have received simple training.

Materials and method

A prospective single-center study was conducted at Hanoi Obstetrics and Gynecology Hospital. Three resident gynecologists trained themselves for two weeks and then received hands-on practice under the supervision of experts for another two weeks. The examiners performed ultrasound on 424 eligible women scheduled for surgery for ovarian tumors and classified the tumors based on IOTA simple rules. The postoperative pathology of ovarian tumors was used as the gold standard.

Results

90.8 % (385/424) of the tumors were benign. Simple rules were applicable in 399/424 (94.1 %) tumors, with a sensitivity of 84.8 % (95 % CI, 70.2-94.3), specificity of 98.9 % (95 % CI, 97.5-99.7), positive predictive value of 87.5 % (95 % CI, 73.3-95.9), and negative predictive value of 98.6 % (95 % CI, 97.1-99.5). The sensitivity of IOTA simple rules was higher in postmenopausal women (91.7 % vs. 81.0 %), while the specificity was higher in premenopausal women (99.4 % vs. 95.8 %). Accuracy was 100 % in all ten pregnant women were assessed using these rules.

Conclusion

In conclusion, in the hands of non-expert examiners who were trained thoroughly, IOTA simple rules are a simple and efficient tool for clinical practice in centers where expert radiologists in gynecology are not always available. The training program is simple and could be applied widely in other clinical centers. Further studies are necessary to evaluate the effectiveness of the IOTA simple rules in assessing ovarian tumors among pregnant women.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

O-RADS combined with contrast-enhanced ultrasound in risk stratification of adnexal masses

BACKGROUND

Ovarian-Adnexal Reporting and Data System (O-RADS) for ultrasound is a lexicon and risk stratification system that includes all risk categories and relevant management recommendation. It has high sensitivity in diagnosing malignant adnexal tumors, but the specificity is lower.

OBJECTIVE

To explore the value of O-RADS combined with contrast-enhanced ultrasound (CEUS) in risk stratification of adnexal masses.

METHODS

A retrospective study was performed on 85 patients with 100 adnexal masses that preoperatively underwent conventional ultrasound as well as CEUS examination and obtained the postoperative pathological results. The masses were classified into O-RADS2, 3, 4, and 5 by conventional ultrasound. After contrast enhancement, the classification of O-RADS was adjusted according to CEUS imaging features. The O-RADS 2 and 3 lesions with suspected malignant features like irregular blood vessels or internal inhomogeneous hyperenhancement were upgraded to O-RADS 4, and the O-RADS 4 lesions with the above features were upgraded to O-RADS 5. The O-RADS 4 lesions with suspicious benign angiographic features like a regular vessel, interior hypoenhancement or non-enhancement were downgraded to O-RADS 3; the O-RADS 5 lesions with rim ring-enhancement and interior non-enhancement were downgraded to O-RADS 3. The sensitivity, specificity, accuracy, PPV, NPV, and AUC of the two methods were compared, taking pathological results as the gold standard.

RESULTS

The sensitivity, specificity, accuracy, PPV, NPV, and AUC of O-RADS and O-RADS combined with CEUS in the diagnosis of malignant adnexal tumors were 96.6%, 66.2%, 75.0%, 53.8%, 97.9%, 0.910 and 96.6%, 91.5%, 93.0%, 82.4%, 98.5%, 0.962, respectively. The specificity, accuracy, PPV, and AUC of O-RADS combined with CEUS were considerably higher than those of O-RADS (P < 0.01). Furthermore, both methods had excellent sensitivity and NPV but there were no significant differences between them(P > 0.05).

CONCLUSION

Combination of O-RADS and CEUS can significantly improve the specificity and PPV in diagnosing malignant adnexal tumors. It seems promising in the clinical application of risk stratification of adnexal masses.

Adnexal Lesion Imaging: Past, Present, and Future

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation. Since the inclusion of imaging in the evaluation of adnexal lesions in the 1970s, the rate of surgery for benign lesions has decreased. More recently, data-driven Ovarian-Adnexal Reporting and Data System (O-RADS) scoring systems for US and MRI with standardized lexicons have been developed to allow for assignment of a cancer risk score, with the goal of further decreasing unnecessary interventions while expediting the care of patients with ovarian cancer. US is used as the initial modality for the assessment of adnexal lesions, while MRI is used when there is a clinical need for increased specificity and positive predictive value for the diagnosis of cancer. This article will review how the treatment of adnexal lesions has changed due to imaging over the decades; the current data supporting the use of US, CT, and MRI to determine the likelihood of cancer; and future directions of adnexal imaging for the early detection of ovarian cancer.

Ovarian-Adnexal Reporting and Data System for Magnetic Resonance Imaging (O-RADS MRI): Genesis and Future Directions

Imaging plays an important role in characterizing and risk-stratifying commonly encountered adnexal lesions. Recently, the American College of Radiology (ACR) released the Ovarian-Adnexal Reporting and Data System (O-RADS) for ultrasound and subsequently for magnetic resonance imaging (MRI). The goal of the recently developed ACR O-RADS MRI risk stratification system is to improve the quality of imaging reports as well as the reproducibility of evaluating adnexal lesions on MRI. This review focuses on exploring this new system and its future refinements.

Manifestations of Ovarian Cancer in Relation to Other Pelvic Diseases by MRI

Imaging plays a pivotal role in the diagnostic approach of women with suspected ovarian cancer. MRI is widely used for preoperative characterization and risk stratification of adnexal masses. While epithelial ovarian cancer (EOC) has typical findings on MRI; there are several benign and malignant pelvic conditions that may mimic its appearance on imaging. Knowledge of the origin and imaging characteristics of a pelvic mass will help radiologists diagnose ovarian cancer promptly and accurately. Finally, in special subgroups, including adolescents and gravid population, the prevalence of various ovarian tumors differs from that of the general population and there are conditions which uniquely manifest during these periods of life.

Nomogram based on the O-RADS for predicting the malignancy risk of adnexal masses with complex ultrasound morphology

OBJECTIVE

The accurate preoperative differentiation of benign and malignant adnexal masses, especially those with complex ultrasound morphology, remains a great challenge for junior sonographers. The purpose of this study was to develop and validate a nomogram based on the Ovarian-Adnexal Reporting and Data System (O-RADS) for predicting the malignancy risk of adnexal masses with complex ultrasound morphology.

METHODS

A total of 243 patients with data on adnexal masses with complex ultrasound morphology from January 2019 to December 2020 were selected to establish the training cohort, while 106 patients with data from January 2021 to December 2021 served as the validation cohort. Univariate and multivariate analyses were used to determine independent risk factors for malignant tumors in the training cohort. Subsequently, a predictive nomogram model was developed and validated in the validation cohort. The calibration, discrimination, and clinical net benefit of the nomogram model were assessed separately by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, we compared this model to the O-RADS.

RESULTS

The O-RADS category, an elevated CA125 level, acoustic shadowing and a papillary projection with color Doppler flow were the independent predictors and were incorporated into the nomogram model. The area under the ROC curve (AUC) of the nomogram model was 0.958 (95% CI, 0.932-0.984) in the training cohort. The specificity and sensitivity were 0.939 and 0.893, respectively. This nomogram also showed good discrimination in the validation cohort (AUC = 0.940, 95% CI, 0.899-0.981), with a sensitivity of 0.915 and specificity of 0.797. In addition, the nomogram model showed good calibration efficiency in both the training and validation cohorts. DCA indicated that the nomogram was clinically useful. Furthermore, the nomogram model had higher AUC and net benefit than the O-RADS.

CONCLUSION

The nomogram based on the O-RADS showed a good predictive ability for the malignancy risk of adnexal masses with complex ultrasound morphology and could provide help for junior sonographers.

Gynecologic Imaging and Reporting Data System for classifying adnexal masses

INTRODUCTION

To perform a systematic review and meta-analysis of the diagnostic performance of the so-called Gynecologic Imaging and Report Data System (GI-RADS) for classifying adnexal masses.

EVIDENCE ACQUISITION

A search for studies reporting about the use of GI-RADS system for classifying adnexal masses from January 2009 to December 2021 was performed in Medline (Pubmed), Google Scholar, Scopus, Cochrane, and Web of Science databases. Pooled sensitivity, specificity, positive and negative likelihood ratios and diagnostic odd ratio (DOR) were calculated. Studies' quality was evaluated using QUADAS-2.

EVIDENCE SYNTHESIS

We identified 510 citations. Ultimately, 26 studies comprising 7350 masses were included. Mean prevalence of ovarian malignancy was 26%. The risk of bias was high in eight studies for domain "patient selection" and low for "index test," "reference test" domains for all studies. Overall, pooled estimated sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio and DOR of GI-RADS system for classifying adnexal masses were 94% (95% confidence interval [CI]=91-96%), 90% (95% CI=87-92%), 9.1 (95% CI=7.0-11.9), and 0.07 (95% CI=0.05-0.11), and 132 (95% CI=78-221), respectively. Heterogeneity was high for both sensitivity and specificity. Meta-regression showed that multiple observers and study's design explained this heterogeneity among studies.

CONCLUSIONS

GI-RADS system has a good diagnostic performance for classifying adnexal masses.

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.

IETA Ultrasonic Features Combined with GI-RADS Classification System and Tumor Biomarkers for Surveillance of Endometrial Carcinoma: An Innovative Study

Objectives: We were the first to combine IETA ultrasonic features with GI-RADS and tumor biomarkers for the surveillance of endometrial carcinoma. The aim was to evaluate the efficacy of single IETA ultrasonography GI-RADS classification and combined tumor biomarkers in differentiating benign and malignant lesions in the uterine cavity and endometrium. Methods: A total of 497 patients with intrauterine and endometrial lesions who had been treated surgically between January 2017 and December 2021 were enrolled; all of them had undergone ultrasound examinations before surgery. We analyzed the correlation between the terms of ultrasonic signs of the uterine cavity and endometrial lesions defined by the expert consensus of IETA and the benign and malignant lesions and then classified these ultrasonic signs by GI-RADS. In addition, the tumor biomarkers CA125, CA15-3, CA19-9 and HE4 were combined by adjusting the classification. The results of the comprehensive analysis were compared with pathological results to analyze their diagnostic efficacy. Results: (1) The statistic analysis confirmed that there were seven independent predictors of malignant lesions, including thickened endometrium (premenopause ≥ 18.5 mm, postmenopause ≥ 15.5 mm), non-uniform endometrial echogenicity (heterogeneous with irregular cysts), endometrial midline appearance (not defined), the endometrial-myometrial junction (interrupted or not defined), intracavitary fluid (ground glass or "mixed" echogenicity), color score (3~4 points) and vascular pattern (focal origin multiple vessels or multifocal origin multiple vessels). (2) In traditional ultrasound GI-RADS (U-T-GI-RADS), if category 4a was taken as the cut-off value of benign and malignant, the diagnostic sensitivity, specificity, PPV, NPV and diagnostic accuracy were 97.2%, 65.2%, 44.0%, 98.8% and 72.2%, respectively, and the area under the ROC curve (AUC) was 0.812. If 4b was taken as the cut-off value, the diagnostic sensitivity, specificity, PPV, NPV diagnostic accuracy and AUC were 88.1%, 92.0%, 75.6%, 96.5% and 91.2%, 0.900, respectively. The diagnostic sensitivity, specificity, PPV, NPV diagnostic accuracy and AUC were 75.2%, 98.5%, 93.2%, 93.4%, 93.4% and 0.868, respectively, when taking category 5 as the cutoff point. In modified ultrasound GI-RADS (U-M-GI-RADS), if 4a was taken as the cut-off value, The diagnostic efficacy was the same as U-T-GI-RADS. If 4b was taken as the cut-off value, the diagnostic sensitivity, specificity, PPV, NPV, diagnostic accuracy and AUC were 88.1%, 92.3%, 76.2%, 96.5%, 91.3% and 0.902, respectively. If 4c was taken as the cutoff point, the diagnostic sensitivity, specificity, PPV, NPV diagnostic accuracy and AUC were 75.2%, 98.7%, 94.3%, 93.4%, 93.6% and 0.870, respectively. The diagnostic sensitivity, specificity, PPV, NPV diagnostic accuracy and AUC were 66.1%, 99.7%, 98.6%, 91.3%, 92.4% and 0.829, respectively, if taking category 5 as the cutoff point. (3) In the comprehensive diagnostic method of U-T-GI-RADS combined tumor biomarkers results, the AUC of class 4a, 4b and 5 as the cutoff value was 0.877, 0.888 and 0.738, respectively. The AUC of class 4a, 4b, 4c and 5 as the cutoff value in the comprehensive diagnostic method of U-M-GI-RADS combined tumor biomarkers results was 0.877, 0.888, 0.851 and 0.725, respectively. There was no significant difference in diagnostic efficiency between the two comprehensive diagnostic methods. Conclusions: In this study, no matter which diagnostic method was used, the best cutoff value for predicting malignant EC was ≥GI-RADS 4b. The GI-RADS classification had good performance in discriminating EC. The tumor biomarkers, CA125, CA19-9, CA15-3 and HE4, could improve the diagnostic efficacy for preoperative endometrial carcinoma assessment.

Comparison of the O-RADS and ADNEX models regarding malignancy rate and validity in evaluating adnexal lesions

OBJECTIVE

This study aimed to compare the ability of the O-RADS and ADNEX models to classify benign or malignant adnexal lesions.

METHODS

This retrospective single-center study included women who underwent surgery for adnexal lesions. Two gynecologists independently categorized the adnexal lesions according to the O-RADS and ADNEX models. Four additional readers were included to validate the new quick-access O-RADS flowchart.

RESULTS

Among the 322 patients included in this study, 264 (82.0%) had a benign diagnosis, and 58 (18.0%) had a malignant diagnosis. The malignant rates of O-RADS 2, O-RADS 3, O-RADS 4, and O-RADS 5 were 0%, 3.0%, 37.7%, and 78.9%, respectively. The AUC of the O-RADS in the 322 patients was 0.93. On comparing the O-RADS and ADNEX models in the remaining 281 patients, the AUCs of the O-RADS, ADNEX model with CA125, and ADNEX model without CA125 were 0.92, 0.95, and 0.94, respectively. When setting a uniform cutoff of ≥ 10% (≥ O-RADS 4) to predict malignancy, the O-RADS had higher sensitivity than the ADNEX model (96.6% vs. 91.4%), and relatively similar specificity. In addition, the readers with the quick-access flowchart spent less time categorizing O-RADS than the readers with only the original O-RADS table (mean analysis time: 99 min 15 s vs. 111 min 55 s).

CONCLUSIONS

The O-RADS classification of the adnexal lesions as benign or malignant was comparable to that of the ADNEX model and had higher sensitivity at the 10% cutoff value. A quick-access O-RADS flowchart was helpful in O-RADS categorization and might shorten the analysis time.

KEY POINTS

• Both O-RADS and ADNEX models had good diagnostic performance in distinguishing adnexal malignancy, and O-RADS had higher sensitivity than ADNEX model in uniform 10% cutoff to predict malignancy. • Quick-access O-RADS flowchart was developed to help review O-RADS classification and might help reduce the analysis time.

Application of O-RADS Ultrasound Lexicon-Based Logistic Regression Analysis Model in the Diagnosis of Solid Component-Containing Ovarian Malignancies

Objective

To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian-Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component-containing mass benignancy/malignancy.

Methods

We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. Thirteen ovarian tumor-related parameters in the benign and malignant ovarian tumor groups were selected for univariate analyses. Statistically significant parameters were included in multivariate logistic regression analyses to construct a logistic regression diagnosis model, and the diagnostic performance of the model in predicting ovarian malignancies was calculated.

Results

Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid component, external contour, whether acoustic shadows were present in the solid component, number of papillae, vascularity, presence/absence of ascites, and presence/absence of peritoneal thickening or nodules between the benign ovarian tumor and malignancy groups (p < 0.05). Logistic regression analyses showed that maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites were included in the logistic regression model, and the area under the receiver operating characteristic curve for this regression model in predicting ovarian malignancy was 0.962 (95% confidence interval: 0.933~0.990; p < 0.001). Logit (p) ≥ −0.02 was used as the cutoff value, and the prediction accuracy, sensitivity, specificity, positive predictive value, and negative predictive values were 93.6%, 86.8%, 98.1%, 96.7%, and 91.9%, respectively.

Conclusion

The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites can help in determining the benignancy/malignancy of solid component-containing masses.

Contrast-enhanced ultrasonography for differential diagnosis of adnexal masses

Background

Quantitative contrast-enhanced ultrasonography parameters are affected by various factors. We evaluated corrected quantitative contrast enhanced ultrasonography in differentiating benign adnexal tumors from malignant tumors.

Methods

Patients with adnexal masses who underwent conventional and contrast-enhanced ultrasonography were included. Contrast-enhanced ultrasonography parameters such as base intensity, arrival time, peak intensity, time to peak intensity, ascending slope, and descending slope were measured. Corrected (time to peak intensity − arrival time) _mass/(time to peak intensity − arrival time) _uterus and (peak intensity − base intensity) _mass/(peak intensity − base intensity) _uterus were calculated. Lesions were confirmed by pathologic examination of surgical specimens.

Results

This study included 31 patients with 35 adnexal lesions including 20 (57.10%) benign and 15 (42.90%) malignant lesions. The corrected contrast-enhanced ultrasonography quantitative parameters in lesions were statistically different between malignant and benign groups (P<0.05). The optimal cut-off value for (time to peak intensity − arrival time) _mass/(time to peak intensity − arrival time) _uterus, ascending slope, and (peak intensity − base intensity) _mass/(peak intensity − base intensity) _uterus, and descending slope for differentiating malignant adnexal masses from benign tumors were 1.05 (area under curve: 0.93, P<0.05), 1.11 (area under curve: 0.83, P<0.05), 0.82 (area under curve: 0.73, P<0.05), and −0.27 (area under curve: 0.66, P=0.16), with sensitivity and specificity of 93.33% and 85.00%, 86.67% and 75.00%, 86.67% and 60.00%, and 54.55% and 66.67%, respectively.

Conclusions

Corrected contrast-enhanced ultrasonography parameters provide practical differential diagnosis value of adnexal lesions with high reliability for sonologists.

Interobserver agreement between eight observers using IOTA simple rules and O-RADS lexicon descriptors for adnexal masses

PURPOSE

To evaluate interobserver agreement in assigning imaging features and classifying adnexal masses using the IOTA simple rules versus O-RADS lexicon and identify causes of discrepancy.

METHODS

Pelvic ultrasound (US) examinations in 114 women with 118 adnexal masses were evaluated by eight radiologists blinded to the final diagnosis (4 attendings and 4 fellows) using IOTA simple rules and O-RADS lexicon. Each feature category was analyzed for interobserver agreement using intraclass correlation coefficient (ICC) for ordinal variables and free marginal kappa for nominal variables. The two-tailed significance level (a) was set at 0.05.

RESULTS

For IOTA simple rules, interobserver agreement was almost perfect for three malignant lesion categories (M2-4) and substantial for the remaining two (M1, M5) with k-values of 0.80-0.82 and 0.68-0.69, respectively. Interobserver agreement was almost perfect for two benign feature categories (B2, B3), substantial for two (B4, B5) and moderate for one (B1) with k-values of 0.81-0.90, 0.69-0.70 and 0.60, respectively. For O-RADS, interobserver agreement was almost perfect for two out of ten feature categories (ascites and peritoneal nodules) with k-values of 0.89 and 0.97. Interobserver agreement ranged from fair to substantial for the remaining eight feature categories with k-values of 0.39-0.61. Fellows and attendings had ICC values of 0.725 and 0.517, respectively.

CONCLUSION

O-RADS had variable interobserver agreement with overall good agreement. IOTA simple rules had more uniform interobserver agreement with overall excellent agreement. Greater reader experience did not improve interobserver agreement with O-RADS.

Diagnostic Performance of the Ovarian-Adnexal Reporting and Data System (O-RADS) Ultrasound Risk Score in Women in the United States

IMPORTANCE

The American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) risk scoring system has been studied in a selected population of women referred for suspected or known adnexal lesions. This population has a higher frequency of malignant neoplasms than women presenting to radiology departments for pelvic ultrasonography for a variety of indications, potentially impacting the diagnostic performance of the risk scoring system.

OBJECTIVE

To evaluate the risk of malignant neoplasm and diagnostic performance of O-RADS US risk scoring system in a multi-institutional, nonselected cohort.

DESIGN, SETTING, AND PARTICIPANTS

This multi-institutional cohort study included a population of nonselected women in the United States who presented to radiology departments for routine pelvic ultrasonography between 2011 and 2014, with pathology confirmation imaging follow up or 2 years of clinical follow up.

EXPOSURE

Analysis of 1014 adnexal lesions using the O-RADS US risk stratification system.

MAIN OUTCOMES AND MEASURES

Frequency of ovarian cancer and diagnostic performance of the O-RADS US risk stratification system.

RESULTS

This study included 913 women with 1014 adnexal lesions. The mean (SD) age of the patients was 42.4 (13.9 years), and 674 of 913 (73.8%) were premenopausal. The overall frequency of malignant neoplasm was 8.4% (85 of 1014 adnexal lesions). The frequency of malignant neoplasm for O-RADS US 2 was 0.5% (3 of 657 lesions; <1% expected); O-RADS US 3, 4.5% (5 of 112 lesions; <10% expected); O-RADS US 4, 11.6% (18 of 155; 10%-50% expected); and O-RADS 5, 65.6% (59 of 90 lesions; >50% expected). O-RADS US 4 was the optimum cutoff for diagnosing cancer with sensitivity of 90.6% (95% CI, 82.3%-95.9%), specificity of 81.9% (95% CI, 79.3%-84.3%), positive predictive value of 31.4% (95% CI, 25.7%-37.7%) and negative predictive value of 99.0% (95% CI, 98.0%-99.6%).

CONCLUSIONS AND RELEVANCE

In this cohort study of a nonselected patient population, the O-RADS US risk stratification system performed within the expected range as published by the ACR O-RADS US committee. The frequency of malignant neoplasm was at the lower end of the published range, partially because of the lower prevalence of cancer in a nonselected population. However, a high negative predictive value was maintained, and when a lesion can be classified as an O-RADS US 2, the risk of cancer is low, which is reassuring for both clinician and patient.

Comparison of O-RADS, GI-RADS, and ADNEX for Diagnosis of Adnexal Masses: An External Validation Study Conducted by Junior Sonologists

OBJECTIVE

To externally validate the Ovarian-adnexal Reporting and Data System (O-RADS) and evaluate its performance in differentiating benign from malignant adnexal masses (AMs) compared with the Gynecologic Imaging Reporting and Data System (GI-RADS) and Assessment of Different NEoplasias in the adneXa (ADNEX).

METHODS

A retrospective analysis was performed on 734 cases from the Second Affiliated Hospital of Fujian Medical University. All patients underwent transvaginal or transabdominal ultrasound examination. Pathological diagnoses were obtained for all the included AMs. O-RADS, GI-RADS, and ADNEX were used to evaluate AMs by two sonologists, and the diagnostic efficacy of the three systems was analyzed and compared using pathology as the gold standard. We used the kappa index to evaluate the inter-reviewer agreement (IRA).

RESULTS

A total of 734 AMs, including 564 benign masses, 69 borderline masses, and 101 malignant masses were included in this study. O-RADS (0.88) and GI-RADS (0.90) had lower sensitivity than ADNEX (0.95) (P < .05), and the PPV of O-RADS (0.98) was higher than that of ADNEX (0.96) (P < .05). These three systems showed good IRA.

CONCLUSION

O-RADS, GI-RADS, and ADNEX showed little difference in diagnostic performance among resident sonologists. These three systems have their own characteristics and can be selected according to the type of center, access to patients' clinical data, or personal comfort.

Quantitative analysis of the MRI features in the differentiation of benign, borderline, and malignant epithelial ovarian tumors

Objective

This study aims to investigate the value of the quantitative indicators of MRI in the differential diagnoses of benign, borderline, and malignant epithelial ovarian tumors (EOTs).

Materials and methods

The study population comprised 477 women with 513 masses who underwent MRI and operation, including benign EOTs (BeEOTs), borderline EOTs (BEOTs), and malignant EOTs (MEOTs). The clinical information and MRI findings of the three groups were compared. Then, multivariate logistic regression analysis was performed to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were also used to evaluate the diagnostic performance of the quantitative indicators of MRI and clinical information in differentiating BeEOTs from BEOTs or differentiating BEOTs from MEOTs.

Results

The MEOTs likely involved postmenopausal women and showed higher CA-125, HE4 levels, ROMA indices, peritoneal carcinomatosis and bilateral involvement than BeEOTs and BEOTs. Compared with BEOTs, BeEOTs and MEOTs appeared to be more frequently oligocystic (P < 0.001). BeEOTs were more likely to show mild enhancement (P < 0.001) and less ascites (P = 0.003) than BEOTs and MEOTs. In the quantitative indicators of MRI, BeEOTs usually showed thin-walled cysts and no solid component. BEOTs displayed irregular thickened wall and less solid portion. MEOTs were more frequently characterized as solid or predominantly solid mass (P < 0.001) than BeEOTs and BEOTs. The multivariate logistic regression analysis showed that volume of the solid portion (P = 0.006), maximum diameter of the solid portion (P = 0.038), enhancement degrees (P < 0.001), and peritoneal carcinomatosis (P = 0.011) were significant indicators for the differential diagnosis of the three groups. The area under the curves (AUCs) of above indicators and combination of four image features except peritoneal carcinomatosis for the differential diagnosis of BeEOTs and BEOTs, BEOTs and MEOTs ranged from 0.74 to 0.85, 0.58 to 0.79, respectively.

Conclusion

In this study, the characteristics of MRI can provide objective quantitative indicators for the accurate imaging diagnosis of three categories of EOTs and are helpful for clinical decision-making. Among these MRI characteristics, the volume, diameter, and enhancement degrees of the solid portion showed good diagnostic performance.

Ultrasound Assessment of Adnexal Pathology: Standardized Methods and Different Levels of Experience

Background and objectives: An expert’s subjective assessment is still the most reliable evaluation of adnexal pathology, thus raising the need for methods less dependent on the examiner’s experience. The aim of this study was to evaluate the performance of standardized methods when applied by examiners with different levels of experience and to suggest the most suitable method for less-experienced gynecologists. Materials and methods: This single-center retrospective study included 50 cases of histologically proven first-time benign or malignant adnexal pathology. Three examiners evaluated the same transvaginal ultrasound images: an expert (level III), a 4th year resident in gynecology (level I), and a final year medical student after basic training (labeled as level 0). The assessment methods included subjective evaluation, Simple Rules (SR) with and without algorithm, ADNEX and Gynecologic Imaging Reporting and Data System (GI-RADS) models. Sensitivity, specificity, accuracy, positive and negative predictive values with 95% confidence interval were calculated. Results: Out of 50 cases, 33 (66%) were benign and 17 (34%) were malignant adnexal masses. Using only SR, level III could classify 48 (96%), level I—41 (82%) and level 0—40 (80%) adnexal lesions. Using SR and algorithm, the performance improved the most for all levels and yielded sensitivity and specificity of 100% for level III, 100% and 97% for level I, 94.4% and 100% for level 0, respectively. Compared to subjective assessment, ADNEX lowered the accuracy of level III evaluation from 97.9% to 88% and GI-RADS had no impact. ADNEX and GI-RADS improved the sensitivity up to 100% for the less experienced; however, the specificity and accuracy were notably decreased. Conclusions: SR and SR+ algorithm have the most potential to improve not only sensitivity, but also specificity and accuracy, irrespective of the experience level. ADNEX and GI-RADS can yield sensitivity of 100%; however, the accuracy is decreased.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumors, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group, and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the pre-operative diagnosis of ovarian tumors, including imaging techniques, biomarkers, and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the pre-operative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the pre-operative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumours

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumours, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumours and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumours and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

Radiomics and radiogenomics in ovarian cancer: a literature review

Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.

Prediction Models of Adnexal Masses: State-of-the-Art Review

Importance

Several predictive models and scoring systems have been developed to differentiate between benign and malignant ovarian masses, in order to guide effective management. These models use combinations of patient characteristics, ultrasound markers, and biochemical markers.

Objective

The aim of this study was to describe, compare, and prioritize, according to their strengths and qualities, all the adnexal prediction models.

Evidence Acquisition

This was a state-of-the-art review, synthesizing the findings of the current published literature on the available prediction models of adnexal masses.

Results

The existing models include subjective assessment by expert sonographers, the International Ovarian Tumor Analysis models (logistic regression models 1 and 2, Simple Rules, 3-step strategy, and ADNEX [Assessment of Different NEoplasias in the adneXa] model), the Risk of Malignancy Index, the Risk of Malignancy Ovarian Algorithm, the Gynecologic Imaging Reporting and Data System, and the Ovarian-Adnexal Reporting and Data System. Overall, subjective assessment appears to be superior to all prediction models. However, the International Ovarian Tumor Analysis models are probably the best available methods for nonexpert examiners. The Ovarian-Adnexal Reporting and Data System is an international approach that incorporates both the common European and North American approaches, but still needs to be validated.

Conclusions

Many prediction models exist for the assessment of adnexal masses. The adoption of a particular model is based on local guidelines, as well as sonographer's experience. The safety of expectant management of adnexal masses with benign ultrasound morphology is still under investigation.

The diagnostic performance of the Gynecologic Imaging Reporting and Data System (GI-RADS) in adnexal masses

Background

Adnexal masses, mostly benign, are common in the female genital system. However, adnexal masses are the leading cause of death among women with gynecologic cancer. Ultrasound is a common imaging method for diagnosing adnexal masses. Gynecologic Imaging Reporting and Data System (GI-RADS) is a useful diagnostic tool based on objective ultrasound features to diagnose the malignancy of the female genital system. Therefore, we conducted a meta-analysis to evaluate the ability of GI-RADS to differentiate adnexal masses.

Methods

Published articles were searched in PubMed, Medline, and Embase from 1990 to February 2020. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio, and area under the curve (AUC) were estimated via the extracted data from the selected studies.

Results

Ten studies and 2,474 patients were included in this meta-analysis. The pooled sensitivity of selected studies was 0.95 [95% confidence intervals (CI): 0.94-0.97], and the pooled specificity was 0.86 (95% CI: 0.84-0.88). The pooled NLR and PLR were 0.06 (95% CI: 0.04-0.10), and 8.30 (95% CI: 4.93-13.97), respectively. Moreover, the pooled diagnostic odds ratio for GI-RADS was 174.59 (95% CI: 76.70-397.42), and the AUC was 0.9806.

Conclusions

This research indicates that GI-RADS might be a valuable tool to distinguish malignancies from adnexal masses.

Ovarian-Adnexal Reporting Lexicon for MRI: A White Paper of the ACR Ovarian-Adnexal Reporting and Data Systems MRI Committee

MRI is used in the evaluation of ovarian and adnexal lesions. MRI can further characterize lesions seen on ultrasound to help decrease the number of false-positive lesions and avoid unnecessary surgery in benign lesions. Currently, the reporting of ovarian and adnexal findings on MRI is inconsistent because of the lack of standardized descriptor terminology. The development of uniform reporting descriptors can lead to improved interpretation agreement and communication between radiologists and referring physicians. The Ovarian-Adnexal Reporting and Data Systems MRI Committee was formed under the direction of the ACR to create a standardized lexicon for adnexal lesions with the goal of improving the quality and consistency of imaging reports. This white paper describes the consensus process in the creation of a standardized lexicon for ovarian and adnexal lesions for MRI and the resultant lexicon.

Diagnostic performance of GI-RADS reporting system in evaluation of adnexal masses

Background

Transvaginal and pelvic ultrasound are considered the primary imaging modality in evaluating adnexal masses. Gynaecologic Imaging Reporting and Data System (GI-RADS) depends on different ultrasound patterns and criteria adopted by the International Ovarian Tumour Analysis (IOTA) group. The current study aimed to detect the diagnostic accuracy of the GI-RADS classification in evaluating adnexal masses. In this prospective cross-sectional study, a total of 112 adnexal masses belonging to 100 women, age ranged 12 to 66 years old, were included. The study population was recruited throughout the period between January and November 2017. Ultrasound examination was performed to all patients; different US and Doppler criteria were assessed.

Results

Out of the 112 lesions, 36 (32.1%) were GI-RADS 2, 32 (28.6%) GI-RADS 3, 13 (11.6%) GI-RADS 4, and 31 (27.7%) GI-RADS 5. The GI-RADS classification showed sensitivity 97%, specificity 84.8%, positive predictive value (PPV) 72.7%, negative predictive value (NPV) 98.5%, and accuracy 88.4%.

Conclusion

The GI-RADS reporting system carried a high sensitivity in identifying adnexal masses at high risk of malignancy. The increased number of benign lesions misclassified as GI-RADS 4 required additional markers to improve the specificity in GI-RADS classification.

Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses

OBJECTIVE

The American College of Radiology (ACR) recently published the ovarian-adnexal reporting and data system (O-RADS) to provide guidelines to physicians who interpret ultrasound (US) examinations of adnexal masses (AM). This study aimed to compare the O-RADS with two other well-established US classification systems for diagnosis of AM.

METHODS

This retrospective multicenter study between May 2016 and December 2019 assessed consecutive women with AM detected by the US. Five experienced consultant radiologists independently categorized each AM according to O-RADS, gynecologic imaging reporting and data system (GI-RADS), and international ovarian tumor analysis (IOTA) simple rules. Pathology and adequate follow-up were used as reference standards for calculating the validity of three US classification systems for diagnosis of AM. Kappa statistics were used to assess the inter-reviewer agreement (IRA).

RESULTS

A total of 609 women (mean age, 48 ± 13.7 years; range, 18-72 years) with 647 AM were included. Of the 647 AM, 178 were malignant and 469 were benign. Malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. O-RADS had significantly higher sensitivity for malignancy than GI-RAD and IOTA (p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (p > 0.05). O-RADS, GI-RADS, and IOTA showed similar overall IRA (κ = 0.77, 0.69, and 0.63, respectively) with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA.

CONCLUSIONS

O-RADS compares favorably with GI-RADS and IOTA. O-RADS had higher sensitivity than GI-RADS and IOTA simple rules with relatively similar specificity and reliability.

KEY POINTS

• The malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. • The O-RADS had significantly higher sensitivity for malignancy than GI-RADS and IOTA (96.8% vs 92.7% and 92.1%; p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (92.8% vs 93.6% and 93.2%, respectively; p > 0.05). • The O-RADS, GI-RADS, and IOTA showed similar overall inter-reviewer agreement (IRA) (κ = 0.77, 0.69, and 0.63, respectively), with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA.

The diagnostic efficacy of Gynecology Imaging Reporting and Data System (GI-RADS): single-center prospective cross-sectional study

Background

To assess the validity and accuracy of GI-RADS classification in the prediction of malignancy and in triaging the management protocol in ovarian lesions.

Results

One hundred fifty-six ovarian lesions were detected in the examined 116 women. The prevalence of malignant tumors was 44%. Overall GI-RADS classification rates were as follows: 41 cases (26.3%) were classified as GI-RADS 1, 26 cases (16 .7%) as GI-RADS 2, 34 cases (21.8%) as GI-RADS 3, 14 cases (8.9%) as GI-RADS 4, and 41 cases (26.3%) as GI-RADS 5. No follow-up was done in GI-RADS 1 patients. A final diagnosis of all GI-RADS 2 ovarian masses such as functional cyst (n = 10), hemorrhagic cysts (n = 8), corpus luteal cysts (n = 6), and some GI-RADS 3 as simple cysts (n = 10) was made by spontaneous resolution of these masses at follow-up after 6 weeks. Fifteen cases of GI-RADS 3 as mature teratoma, serous and mucinous cystadenoma, endometrioma, and ovarian torsion and all GI-RADS 4 and 5 underwent laparoscopic or surgical removal of the ovarian mass with histopathological examination. The diagnostic performance of the GI-RADS in predicting the risk of malignancy in ovarian masses was as follows: 98.11% sensitivity, 95.15% specificity, 91.2% positive predictive value (PPV), 99.2% negative predictive value (NPV), and 20.2 positive likelihood ratio, and the overall accuracy was 96.2% (area under receiver operating curve (AUC) = 0.96, P < 0.001).

Conclusion

GI-RADS classification performs well as a reporting system of the ovarian masses with high diagnostic performance in the prediction of malignancy, and it seems to be a helpful tool in triaging management in patients with ovarian masses.

Trial registration

The trial was registered in the US National Library of Medicine, under clinical trial number NCT03175991. Also, the ethical committee approval number of the Faculty of Medicine, Assiut University, was 17100016 on February 28, 2017.

The Abnormal Ovary: Evolving Concepts in Diagnosis and Management

Ovarian lesions are common and require a consistent approach to diagnosis and management for best patient outcomes. In the past 20 years, there has been an evolution in the approach to abnormal ovarian lesions, with increasing emphasis on reducing surgery for benign disease, standardizing terminology, assessing risk of malignancy through use of evidence-based scoring systems, and triaging suspicious abnormalities to dedicated oncology centers. This article provides an evidence-based review of how these changes in diagnosis and management of ultrasound-detected abnormal ovarian lesions have occurred. Current recommended practices are summarized. The current literature on transvaginal screening for ovarian cancer also is reviewed and summarized.

Combination of GI-RADS and 3D-CEUS for differential diagnosis of ovarian masses

OBJECTIVE

The purpose of this study is to evaluate the efficacy of the combination of gynecologic imaging reporting and data system (GI-RADS) ultrasonographic stratification and three-dimensional contrast-enhanced ultrasonography (3D-CEUS) in order to distinguish malignant from benign ovarian masses.

METHODS

In this study, 102 patients with ovarian masses were examined by both two-dimensional ultrasound(2D-US) and 3D-CEUS. Sonographic features of ovarian masses obtained from 3D-CEUS were analyzed and compared with 2D-US. All patients with ovarian masses were confirmed by operational pathology or long-term follow-up results.

RESULTS

(1)The Chi-square test and multiple Logistic regression analysis confirmed that there were only eight independent predictors of malignant masses, including thick septa (≥3mm), thick papillary projections(≥7mm), solid areas, presence of ascites, central vascularization, contrast enhancement, distribution of contrast agent, and vascular characteristics of the solid part and their odds ratios which were 5.52, 5.39, 4.94, 4.34, 5.92, 7.44, 6.09, and 7.67, respectively (P<0.05). (2)These eight signs were used to combine the GI-RADS with 3D-CEUS scoring system in which the corresponding value of the area under the curve (AUC) was 0.969, which was superior to using GI-RADS lonely (Z-value=1.64, P<0.025). Using 4 points as the cut-off, the scoring system showed the performance was clearly better than using GI-RADS alone (P<0.05). (3) The Kappa value was 0.872 for two different clinicians with equal experience.

CONCLUSIONS

The combination of GI-RADS and 3D-CEUS scoring system would be a more effective method to distinguish malignant from benign ovarian masses.

Diagnostic accuracy of gynecology imaging reporting and data system in evaluation of adnexal lesions

Background:

Considering the increasing incidence rate of ovarian cancer in worldwide and the utility of Gynecologic Imaging Reporting and Data System (GI-RADS) in diagnosing malignant adnexal lesions such as ovarian cancer, we aimed to evaluate the diagnostic performance of this reporting system in differentiating between malignant and benign adnexal lesions.

Materials and Methods:

In this cross-sectional study, women with suspected adnexal lesions were enrolled. For differentiating of malignant adnexal lesions, Grade II and III of GI-RADS system were classified as low risk for malignancy and Grades IV and V as high risk. Results of histopathologic diagnosis were compared with the results of the mentioned GI-RADS system classification, and the diagnosed accuracy of the system was determined. Patients who did not have histopathologic diagnosis were followed up.

Results:

In this study, 197 women with suspected adnexal lesions were evaluated. Frequency of GI-RADS II, III, IV, and V were 34.5% (69 cases), 38.0% (76 cases), 19.5% (39 cases), and 6.5% (13 cases), respectively. According to the low- and high-risk classification of GI-RADS, 72.5% were classified as GI-RADS II and III and 26% as GI-RADS IV and V, respectively. Definitive histopathologic diagnosis was reported for 158 cases. Histopathologic evaluation indicated that 12 (7.6%) of the masses were malignant and 146 (92.6%) were benign. Comparing with the histopathologic diagnosis, the GI-RADS system sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio (LR), and negative LR were 91.6%, 80.82%, 28.2%, 99.1%, 4.77, and 0.10, respectively. The accuracy of the scoring system was 81.64%.

Conclusion:

Our findings indicated that using GI-RADS, we could quantify the risk of malignancy by such a structured as well as simple reporting system so that the system could be useful for clinicians for performing an appropriate clinical management.

Mucinous Neoplasms of the Ovary: Radiologic-Pathologic Correlation

Mucinous neoplasms of the ovary account for 10%-15% of ovarian neoplasms. They may be benign, borderline, or malignant. The large majority are benign or borderline, accounting for 80% and 16%-17%, respectively. Mucinous neoplasms of the ovary most commonly affect women in their 20s to 40s. The clinical manifestation is nonspecific, but most mucinous ovarian neoplasms manifest as large unilateral pelvic masses. At gross pathologic analysis, mucinous ovarian neoplasms appear as large multiloculated cystic masses. The contents of the cyst loculi vary on the basis of differences in internal mucin content. At histologic analysis, mucinous ovarian neoplasms are composed of multiple cysts lined by mucinous epithelium, often resembling gastrointestinal-type epithelium. Imaging evaluation most commonly includes US and/or MRI. The imaging findings parallel the gross pathologic features and include a large, unilateral, multiloculated cystic mass. The cyst loculi vary in echogenicity, attenuation, and signal intensity depending on the mucin content. Mucinous neoplasms of the ovary are staged surgically using the FIGO (International Federation of Gynecology and Obstetrics) staging system. Primary treatment is surgical, with adjuvant chemotherapy considered in the uncommon case of mucinous carcinoma with extraovarian disease. Since most mucinous ovarian neoplasms are benign or borderline, the overall prognosis is excellent.

Imaging and Differential Diagnosis of Ovarian Cancer

Ovarian cancer is the seventh most common cancer affecting women. Despite advances in cancer control and healthcare in general, mortality from ovarian cancer remains unacceptably high due to diagnosis at an advanced stage of the disease. The 5-year survival rate is 47.4% because a majority of ovarian cancers are diagnosed when advanced. Only 14.9% of ovarian cancers are diagnosed when localized where the survival rate is 92.3%. Mortality rate reduction by screening has not been proven in women at an average risk for ovarian cancer. Ultrasound remains the primary modality for assessment of ovarian tumors. The need for standardizing terminology is critical for optimal assessment of the risk of malignancy in an ovarian tumor. The international ovarian tumor analysis group and more recently the American College of Radiology Ovarian - Adnexal Reporting and Data System Committee have published standardized lexicon for ovarian lesions and encourage ultrasound imagers to adopt this standardized terminology. The aim is to apply the lexicon for risk stratification to allow for consistent follow-up and management. Various methodologies have been tested for characterization of adnexal tumors and to assess risk of malignancy preoperatively. Risk assessment models have been studied against the gold standard of a pattern recognition approach and subjective assessment by an experienced imager. The morphologic patterns of ovarian tumors are detailed and features that are more discriminatory than others in suggesting an ovarian malignancy are described. The imaging pathologic correlation for different tumor types is presented. A brief summary of the ovarian cancer pathologic types and staging of cancer is presented. Finally, the current role of transvaginal sonography as a screening modality for ovarian cancer is discussed. Recently published data show encouraging results, that a multimodal approach of screening for ovarian cancer using transvaginal sonography in women with an elevated CA-125 may prove beneficial and cost effective.

Gynecology Imaging Reporting and Data System (GI-RADS): diagnostic performance and inter-reviewer agreement

OBJECTIVE

To evaluate diagnostic performance and inter-reviewer agreement (IRA) of the Gynecologic Imaging Reporting and Data System (GI-RADS) for diagnosis of adnexal masses (AMs) by pelvic ultrasound (US).

PATIENTS AND METHODS

A prospective multicenter study included 308 women (mean age, 41 ± 12.5 years; range, 15-73 years) with 325 AMs detected by US. All US examinations were analyzed, and AMs were categorized into five categories according to the GI-RADS classification. We used histopathology and US follow-up as the reference standards for calculating diagnostic performance of GI-RADS for detecting malignant AMs. The Fleiss kappa (κ) tests were applied to evaluate the IRA of GI-RADS scoring results for predicting malignant AMs.

RESULTS

A total of 325 AMs were evaluated: 127 (39.1%) were malignant and 198 (60.9%) were benign. Of 95 AMs categorized as GI-RADS 2 (GR2), none was malignant; of 94 AMs categorized as GR3, three were malignant; of 13 AMs categorized as GR4, six were malignant; and of 123 AMs categorized as GR5, 118 were malignant. On a lesion-based analysis, the GI-RADS had a sensitivity, a specificity, and an accuracy of 92.9%, 97.5%, and 95.7%, respectively, when regarding only those AMs classified as GR5 for predicting malignancy. Considering combined GR4 and GR5 as a predictor for malignancy, the sensitivity, specificity, and accuracy of GI-RADS were 97.6%, 93.9%, and 95.4%, respectively. The IRA of the GI-RADS category was very good (κ = 0.896). The best cutoff value for predicting malignant AMs was >GR3.

CONCLUSIONS

The GI-RADS is very valuable for improving US structural reports.

KEY POINTS

• There is still a lack of a standard in the assessment of AMs. • GI-RADS is very valuable for improving US structural reports of AMs. • GI-RADS criteria are easy and work at least as well as IOTA.

Assessment of the diagnostic value of using serum CA125 and GI-RADS system in the evaluation of adnexal masses

Cancer antigen 125 (CA125) is a valuable tumor marker for ovarian cancer. Gynecology Imaging Reporting and Data System (GI-RADS) is proved to be effective at identifying the adnexal masses. We investigated whether the combination of these two methods can improve the diagnostic accuracy of ovarian cancer.We retrospectively analyzed preoperative data of 325 patients diagnosed with suspected adnexal mass, 196 patients with benign ovarian masses and 129 with malignant ovarian cancer (stage I: 34, II: 16, III: 61, IV: 18). CA125 was analyzed using the ARCHITECT system, GI-RADS was evaluated according to the International Ovarian Tumor Analysis consensus nomenclature and definitions. Sensitivities and specificities were also calculated for GI-RADS, CA125 and the combinations.The sensitivity, specificity and accuracy of CA125, GI-RADS were 75.97%, 79.59%, 78.15%, and 90.70%, 90.82%,90.77%, the combination data were 94.79%, 96.00%,95.53%. The AUC of combined diagnostic methods was the largest and significantly better compared with each method alone, P < .001). For stage I-II malignancy, GI-RADS as a single method was superior to CA125.Combined use of serum CA 125 and GI-RADS system improved the identification of adnexal masses at high risk of malignancy and could be used for clinical decision-making.

Diagnostic value of the gynecology imaging reporting and data system (GI-RADS) with the ovarian malignancy marker CA-125 in preoperative adnexal tumor assessment

Objectives

The purpose of this study is to assess the preoperative evaluation of an adnexal mass using the GI-RADS classification and to verify whether CA-125 measurement can offer any additional benefits to the GI-RADS-based prediction of ovarian tumor malignancy.

Material and methods

In this study, we assessed a total of 215 women with an adnexal tumor using the GI-RADS classification combined with CA-125 measurement. All adnexal masses underwent histological verification.

Results

Of a total of 215 lesions, we classified 2 lesions as GI-RADS 2 (0.9%), 118 lesions as GI-RADS 3 (54.9%), 86 lesions as GI-RADS 4 (40.0%) and 9 lesions as GI-RADS 5 (4.2%). For GI-RADS 4–5 lesions, the sensitivity, specificity, PPV, NPV, ACC and OR were as follows: 94.3, 72.2, 52.6, 97.5, 77.7%, and 43.3 (CI 12.0–146), respectively. The corresponding parameters resulting from combining the GI-RADS classification with the CA-125 marker were as follows: 66.0, 93.8, 77.8, 89.4, 87.0%, and 29.6 (CI 12.6–69.6), respectively, with p < 0.001. For Ca-125 > 30 IU/mL alone, the results were as follows: 70.0, 80.3, 53.8, 89.1, 77.7%, and 9.5 (4.6–19.6), respectively, with p < 0.0001.

Additionally, 47.8% of the patients had no symptoms, 36.5% had back pain, 5.2% had an increased abdominal size, 4.3% had menstrual irregularities and 2.6% had constipation. There were 152 benign and 18 malignant cases in the low risk group (GIRADS 1–3 and GIRADS 4 + CA-125 < 30 IU/mL) and 10 benign and 35 malignant tumors in the high-risk group (GIRADS 4 + CA125 > 30 IU/mL and GIRADS 5).

Conclusions

GI-RADS classification had good performance in discriminating ovarian tumors. The additional measurement of CA-125 improves the system specificity, PPV and ACC for preoperative adnexal tumor assessment.

Diagnostic performance of the Gynecology Imaging Reporting and Data System for malignant adnexal masses

OBJECTIVE

To evaluate the Gynecology Imaging Reporting and Data System (GI-RADS) for diagnosis of malignant adnexal masses in a Chinese population.

METHODS

A retrospective study was conducted of patients who underwent evaluation of suspected adnexal masses at a hospital in Tianjin, China, between January 1, 2015, and January 31, 2016. Ultrasonographic diagnosis was based on the GI-RADS classification-a standardized summary of imaging data that estimates the risk of malignancy-and compared with the final pathological diagnosis.

RESULTS

Among 242 patients, thick wall, solid papillary projection, solid area, central blood flow, ascites, and GI-RADS classification were associated with malignancy (P<0.05 for all variables). The 263 masses evaluated were classified as GI-RADS 2 (functional cyst; n=65), GI-RADS 3 (benign neoplasm; n=68), GI-RADS 4 (one or two morphological findings suggestive of malignancy; n=101), and GI-RADS 5 (≥3 morphological findings suggestive of malignancy; n=28). Four malignant cases with false-negative findings were misclassified as GI-RADS 3, whereas 24 benign cases with false-positive findings were misclassified as GI-RADS 4. The sensitivity, specificity, false-positive rate, false-negative rate, accuracy, and Youden index of the GI-RADS classification were 96.4%, 84.3%, 18.5%, 3.0%, 89.3%, and 80.7%, respectively.

CONCLUSION

The GI-RADS classification performed well in the diagnosis of malignant adnexal masses.

Clinical management of incidental findings on pelvic adnexal masses

Due to widespread use of pelvic and transvaginal ultrasound in routine gynecological evaluation, the incidental finding of adnexal masses has led to discussions about management in asymptomatic patients regarding the risk of ovarian cancer. Transvaginal ultrasonography remains the modality of choice in the evaluation of suspicious characteristics. The combined analysis of ultrasound morphological parameters with Doppler study, serum carcinoma antigen 125 and investigation of a symptom index may improve diagnosis. Surgical approach should be considered whenever there are suspicious images, rapid growth of cysts, changes in the appearance compared to the initial evaluation or when the patient has symptoms. Future studies on genetic and molecular mechanisms may help explain the pathophysiology of ovarian cancer, improving early diagnosis and treatment.

Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

BACKGROUND

Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant.

OBJECTIVE

We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules.

STUDY DESIGN

This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves.

RESULTS

Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%.

CONCLUSION

Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally.