Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

BACKGROUND

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.

METHODS

We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.

RESULTS

The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.

CONCLUSIONS

The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery

BACKGROUND

Several diagnostic prediction models to help clinicians discriminate between benign and malignant adnexal masses are available. This study is a head-to-head comparison of the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model with that of the Risk of Ovarian Malignancy Algorithm (ROMA).

METHODS

This is a retrospective study based on prospectively included consecutive women with an adnexal tumour scheduled for surgery at five oncology centres and one non-oncology centre in four countries between 2015 and 2019. The reference standard was histology. Model performance for ADNEX and ROMA was evaluated regarding discrimination, calibration, and clinical utility.

RESULTS

The primary analysis included 894 patients, of whom 434 (49%) had a malignant tumour. The area under the receiver operating characteristic curve (AUC) was 0.92 (95% CI 0.88-0.95) for ADNEX with CA125, 0.90 (0.84-0.94) for ADNEX without CA125, and 0.85 (0.80-0.89) for ROMA. ROMA, and to a lesser extent ADNEX, underestimated the risk of malignancy. Clinical utility was highest for ADNEX. ROMA had no clinical utility at decision thresholds <27%.

CONCLUSIONS

ADNEX had better ability to discriminate between benign and malignant adnexal tumours and higher clinical utility than ROMA.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov NCT01698632 and NCT02847832.

Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound

Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

Objectives

To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance.

Design

Systematic review and meta-analysis of external validation studies.

Data sources

Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023.

Eligibility criteria for selecting studies

All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed.

Results

47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125).

Conclusions

The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed.

Systematic review registration

PROSPERO CRD42022373182.

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Subjective assessment and IOTA ADNEX model in evaluation of adnexal masses in patients with history of breast cancer

OBJECTIVE

To evaluate the performance of subjective assessment and the Assessment of Different NEoplasias in the adneXa (ADNEX) model in discriminating between benign and malignant adnexal tumors and between metastatic and primary adnexal tumors in patients with a personal history of breast cancer.

METHODS

This was a retrospective single-center study including patients with a history of breast cancer who underwent surgery for an adnexal mass between 2013 and 2020. All patients had been examined with transvaginal or transrectal ultrasound using a standardized examination technique and all ultrasound reports had been stored and were retrieved for the purposes of this study. The specific diagnosis suggested by the original ultrasound examiner in the retrieved report was analyzed. For each mass, the ADNEX model risks were calculated prospectively and the highest relative risk was used to categorize each into one of five categories (benign, borderline, primary Stage I, primary Stages II-IV or metastatic ovarian cancer) for analysis of the ADNEX model in predicting the specific tumor type. The performance of subjective assessment and the ADNEX model in discriminating between benign and malignant adnexal tumors and between primary and metastatic adnexal tumors was evaluated, using final histology as the reference standard.

RESULTS

Included in the study were 202 women with a history of breast cancer who underwent surgery for an adnexal mass. At histology, 93/202 (46.0%) masses were benign, 76/202 (37.6%) were primary malignancies (four borderline and 72 invasive tumors) and 33/202 (16.3%) were metastases. The original ultrasound examiner classified correctly 79/93 (84.9%) benign adnexal masses, 72/76 (94.7%) primary adnexal malignancies and 30/33 (90.9%) metastatic tumors. Subjective ultrasound evaluation had a sensitivity of 93.6%, specificity of 84.9% and accuracy of 89.6%, while the ADNEX model had higher sensitivity (98.2%) but lower specificity (78.5%), with similar accuracy (89.1%), in discriminating between benign and malignant ovarian masses. Subjective evaluation had a sensitivity of 51.5%, specificity of 88.8% and accuracy of 82.7% in distinguishing metastatic and primary tumors (including benign, borderline and invasive tumors), and the ADNEX model had a sensitivity of 63.6%, specificity of 84.6% and similar accuracy (81.2%).

CONCLUSIONS

The performance of subjective assessment and the ADNEX model in discriminating between benign and malignant adnexal masses in this series of patients with history of breast cancer was relatively similar. Both subjective assessment and the ADNEX model demonstrated good accuracy and specificity in discriminating between metastatic and primary tumors, but the sensitivity was low. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Comparison of performance between O-RADS, IOTA simple rules risk assessment and ADNEX model in the discrimination of ovarian Brenner tumors

PURPOSE

To describe the clinical and sonographic features of ovarian benign Brenner tumor (BBT) and malignant Brenner tumor (MBT), and to compare performance of four diagnostic models in differentiating them.

METHODS

Fifteen patients with BBTs and nine patients with MBTs were retrospectively identified in our institution from January 2003 and December 2021. One ultrasound examiner categorized each mass according to ovarian-adnexal reporting and data system (O-RADS), international ovarian tumor analysis (IOTA) Simple Rules Risk (SR-Risk) assessment and assessment of different neoplasias in the adnexa (ADNEX) models with/without CA125. Receiver operating characteristic curves were generated to compare diagnostic performance.

RESULTS

Patients with MBT had higher CA125 serum level (62.5% vs. 6.7%, P = 0.009) and larger maximum diameter of lesion (89 mm vs. 43 mm, P = 0.009) than did those with BBT. BBT tended to have higher prevalence of calcifications (100% vs. 55.6%, P = 0.012) and acoustic shadowing (93.3% vs. 33.3%, P = 0.004), and lower color scores manifesting none or minimal flow (100.0% vs. 22.2%, P < 0.001). Areas under curves of O-RADS, IOTA SR-Risk and ADNEX models with/without CA125 were 0.896, 0.913, 0.892 and 0.896, respectively. There were no significant differences between them.

CONCLUSION

BBTs are often small solid tumors with sparse color Doppler signals, which contain calcifications with posterior acoustic shadowing. The most common pattern of MBT is a large multilocular-solid or solid mass with irregular tumor borders, and most were moderately or richly vascularized at color Doppler. These four models have excellent performance in distinguishing them.

Intraoperative frozen section in gynaecology cancers with special reference to ovarian tumours: time to "unfreeze" the pitfalls in the path of the Derby horse of Oncology

PURPOSE

In an oncological set up the role of frozen section biopsy is undeniable. They serve as an important tool for surgeon's intraoperative decision making but the diagnostic reliability of intraoperative frozen section may vary from institute to institute. The surgeon should be well aware of the accuracy of the frozen section reports in their setup to enable them to take decisions based on the report. This is why we had conducted a retrospective study at Dr B. Borooah Cancer Institute, Guwahati, Assam, India to find out our institutional frozen section accuracy.

METHODS

The study was conducted from 1st January 2017 to 31st December 2022 (5 years). All gynaecology oncology patients who were operated on during the study period and had an intraoperative frozen section done were included in the study. Patients who had incomplete final histopathological report (HPR) or no final HPR were excluded from the study. Frozen section and final histopathology report were compared and analysed and discordant cases were analysed based on the degree of discordancy.

RESULTS

For benign ovarian disease, the IFS accuracy, sensitivity and specificity are 96.7%, 100% and 93%, respectively. For borderline ovarian disease the IFS accuracy, sensitivity and specificity are 96.7%, 80% and 97.6%, respectively. For malignant ovarian disease the IFS accuracy, sensitivity and specificity are 95.4%, 89.1% and 100%, respectively. Sampling error was the most common cause of discordancy.

CONCLUSION

Intraoperative frozen section may not have 100% diagnostic accuracy but still it is the running horse of our oncological institute.

Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort

Importance

Ultrasonography-based risk models can help nonexpert clinicians evaluate adnexal lesions and reduce surgical interventions for benign tumors. Yet, these models have limited uptake in the US, and studies comparing their diagnostic accuracy are lacking.

Objective

To evaluate, in a US cohort, the diagnostic performance of 3 ultrasonography-based risk models for differentiating between benign and malignant adnexal lesions: International Ovarian Tumor Analysis (IOTA) Simple Rules with inconclusive cases reclassified as malignant or reevaluated by an expert, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX), and Ovarian-Adnexal Reporting and Data System (O-RADS).

Design, Setting, and Participants

This retrospective diagnostic study was conducted at a single US academic medical center and included consecutive patients aged 18 to 89 years with adnexal masses that were managed surgically or conservatively between January 2017 and October 2022.

Exposure

Evaluation of adnexal lesions using the Simple Rules, ADNEX, and O-RADS.

Main Outcomes and Measures

The main outcome was diagnostic performance, including area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Surgery or follow-up were reference standards. Secondary analyses evaluated the models' performances stratified by menopause status and race.

Results

The cohort included 511 female patients with a 15.9% malignant tumor prevalence (81 patients). Mean (SD) ages of patients with benign and malignant adnexal lesions were 44.1 (14.4) and 52.5 (15.2) years, respectively, and 200 (39.1%) were postmenopausal. In the ROC analysis, the AUCs for discriminative performance of the ADNEX and O-RADS models were 0.96 (95% CI, 0.93-0.98) and 0.92 (95% CI, 0.90-0.95), respectively. After converting the ADNEX continuous individualized risk into the discrete ordinal categories of O-RADS, the ADNEX performance was reduced to an AUC of 0.93 (95% CI, 0.90-0.96), which was similar to that for O-RADS. The Simple Rules combined with expert reevaluation had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 91.9% specificity (95% CI, 88.9%-94.3%), and the Simple Rules combined with malignant classification had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 88.1% specificity (95% CI, 84.7%-91.0%). At a 10% risk threshold, ADNEX had 91.4% sensitivity (95% CI, 83.0%-96.5%) and 86.3% specificity (95% CI, 82.7%-89.4%) and O-RADS had 98.8% sensitivity (95% CI, 93.3%-100%) and 74.4% specificity (95% CI, 70.0%-78.5%). The specificities of all models were significantly lower in the postmenopausal group. Subgroup analysis revealed high performances independent of race.

Conclusions and Relevance

In this diagnostic study of a US cohort, the Simple Rules, ADNEX, and O-RADS models performed well in differentiating between benign and malignant adnexal lesions; this outcome has been previously reported primarily in European populations. Risk stratification models can lead to more accurate and consistent evaluations of adnexal masses, especially when used by nonexpert clinicians, and may reduce unnecessary surgeries.

Comparison of Ultrasound Scores in Differentiating between Benign and Malignant Adnexal Masses

Subjective ultrasound assessment by an expert examiner is meant to be the best option for the differentiation between benign and malignant adnexal masses. Different ultrasound scores can help in the classification, but whether one of them is significantly better than others is still a matter of debate. The main aim of this work is to compare the diagnostic performance of some of these scores in the evaluation of adnexal masses in the same set of patients. This is a retrospective study of a consecutive series of women diagnosed as having a persistent adnexal mass and managed surgically. Ultrasound characteristics were analyzed according to IOTA criteria. Masses were classified according to the subjective impression of the sonographer and other ultrasound scores (IOTA simple rules -SR-, IOTA simple rules risk assessment -SRRA-, O-RADS classification, and ADNEX model -with and without CA125 value-). A total of 122 women were included. Sixty-two women were postmenopausal (50.8%). Eighty-one women had a benign mass (66.4%), and 41 (33.6%) had a malignant tumor. The sensitivity of subjective assessment, IOTA SR, IOTA SRRA, and ADNEX model with or without CA125 and O-RADS was 87.8%, 66.7%, 78.1%, 95.1%, 87.8%, and 90.2%, respectively. The specificity for these approaches was 69.1%, 89.2%, 72.8%, 74.1%, 67.9%, and 60.5%, respectively. All methods with similar AUC (0.81, 0.78, 0.80, 0.88, 0.84, and 0.75, respectively). We concluded that IOTA SR, IOTA SRRA, and ADNEX models with or without CA125 and O-RADS can help in the differentiation of benign and malignant masses, and their performance is similar to the subjective assessment of an experienced sonographer.

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

OBJECTIVES

Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors.

METHODS

We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards.

RESULTS

In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set.

CONCLUSION

Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.

Does Combing O-RADS US and CA-125 Improve Diagnostic Accuracy in Assessing Adnexal Malignancy Risk in Women With Different Menopausal Status?

OBJECTIVES

To evaluate the individual and combined performances of the Ovarian-adnexal Reporting and Data System Ultrasound (O-RADS US) and serum cancer antigen 125 (CA-125) in assessing adnexal malignancy risk in women with different menopausal status.

METHODS

This retrospective study included patients with adnexal masses scheduled for surgery based on their preoperative US and histopathology results between January 2018 and January 2020. O-RADS were used to assess adnexal malignancy by two experienced radiologists. The area under the receiver operating characteristic curves (AUCs) were used to compare the accuracy of O-RADS and a combination of O-RADS and CA-125. The weighted κ index was used to evaluate the inter-reviewer agreement.

RESULTS

Overall, the data of 443 lesions in 443 patients were included, involving 312 benign lesions and 131 malignant lesions. There were 361 premenopausal and 82 postmenopausal patients. The inter-reviewer agreement for the two radiologists was very good (weighted κ: 0.833). Combing O-RADS US and CA-125 significantly increased diagnostic accuracy for classifying malignant from benign adnexal masses, compared with O-RADS US alone (AUC: 0.97 vs 0.95, P < .001 for premenopausal population and AUC: 0.93 vs 0.85, P < .001 for postmenopausal population). The AUCs of O-RADS with and without CA-125 ranged from 0.50 to 0.99 for different adnexal pathology subtypes (ie, benign, borderline, Stage I-IV, and metastatic tumors).

CONCLUSION

The addition of CA-125 helps improve discrimination of O-RADS US between benign and malignant adnexal masses, especially in postmenopausal women.

Comparison of ultrasound-based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Objectives

The ADNEX model offered a good diagnostic performance for discriminating adnexal tumors, but research comparing the abilities of the ADNEX model and MRI for characterizing adnexal tumors has not been reported to our knowledge. The aim of this study was to evaluate the diagnostic accuracy of the ultrasound-based ADNEX (Assessment of Different NEoplasias in the adneXa) model in comparison with that of magnetic resonance imaging (MRI) for differentiating benign, borderline and malignant adnexal masses.

Methods

This prospective study included 529 women with adnexal masses who underwent assessment via the ADNEX model and subjective MRI analysis before surgical treatment between October 2019 and April 2022 at two hospitals. Postoperative histological diagnosis was considered the gold standard.

Results

Among the 529 women, 92 (17.4%) masses were diagnosed histologically as malignant tumors, 67 (12.7%) as borderline tumors, and 370 (69.9%) as benign tumors. For the diagnosis of malignancy, including borderline tumors, overall agreement between the ADNEX model and MRI pre-operation was 84.9%. The sensitivity of the ADNEX model of 0.91 (95% confidence interval [CI]: 0.85-0.95) was similar to that of MRI (0.89, 95% CI: 0.84-0.94; P=0.717). However, the ADNEX model had a higher specificity (0.90, 95% CI: 0.87-0.93) than MRI (0.81, 95% CI: 0.77-0.85; P=0.001). The greatest sensitivity (0.96, 95% CI: 0.92-0.99) and specificity (0.94, 95% CI: 0.91-0.96) were achieved by combining the ADNEX model and subjective MRI assessment. While the total diagnostic accuracy did not differ significantly between the two methods (P=0.059), the ADNEX model showed greater diagnostic accuracy for borderline tumors (P<0.001).

Conclusion

The ultrasound-based ADNEX model demonstrated excellent diagnostic performance for adnexal tumors, especially borderline tumors, compared with MRI. Accordingly, we recommend that the ADNEX model, alone or with subjective MRI assessment, should be used for pre-operative assessment of adnexal masses.

Psychological impact of referral to an oncology hospital on patients with an ovarian mass

OBJECTIVES

In patients with an ovarian mass, a risk of malignancy assessment is used to decide whether referral to an oncology hospital is indicated. Risk assessment strategies do not perform optimally, resulting in either referral of patients with a benign mass or patients with a malignant mass not being referred. This process may affect the psychological well-being of patients. We evaluated cancer-specific distress during work-up for an ovarian mass, and patients' perceptions during work-up, referral, and treatment.

METHODS

Patients with an ovarian mass scheduled for surgery were enrolled. Using questionnaires we measured (1) cancer-specific distress using the cancer worry scale, (2) patients' preferences regarding referral (evaluated pre-operatively), and (3) patients' experiences with work-up and treatment (evaluated post-operatively). A cancer worry scale score of ≥14 was considered as clinically significant cancer-specific distress.

RESULTS

A total of 417 patients were included, of whom 220 (53%) were treated at a general hospital and 197 (47%) at an oncology hospital. Overall, 57% had a cancer worry scale score of ≥14 and this was higher in referred patients (69%) than in patients treated at a general hospital (43%). 53% of the patients stated that the cancer risk should not be higher than 25% to undergo surgery at a general hospital. 96% of all patients were satisfied with the overall work-up and treatment. No difference in satisfaction was observed between patients correctly (not) referred and patients incorrectly (not) referred.

CONCLUSIONS

Relatively many patients with an ovarian mass experienced high cancer-specific distress during work-up. Nevertheless, patients were satisfied with the treatment, regardless of the final diagnosis and the location of treatment. Moreover, patients preferred to be referred even if there was only a relatively low probability of having ovarian cancer. Patients' preferences should be taken into account when deciding on optimal cut-offs for risk assessment strategies.

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.

Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer

Objective

This work was designed to investigate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX (Assessment of Different NEoplasias in the adneXa) model combined with human epithelial protein 4 (HE4) for early ovarian cancer (OC) detection.

Methods

A total of 376 women who were hospitalized and operated on in Women and Children’s Hospital of Chongqing Medical University were selected. Ultrasonographic images, cancer antigen-125 (CA 125) levels, and HE4 levels were obtained. All cases were analyzed and the histopathological diagnosis serves as the reference standard. Based on the IOTA ADNEX model post-processing software, the risk prediction value was calculated. We analyzed receiver operating characteristic curves to determine whether the IOTA ADNEX model alone or combined with HE4 provided better diagnostic accuracy.

Results

The area under the curve (AUC) of the ADNEX model alone or combined with HE4 in predicting benign and malignant ovarian tumors was 0.914 (95% CI, 0.881–0.941) and 0.916 (95% CI, 0.883–0.942), respectively. With the cutoff risk of 10%, the ADNEX model had a sensitivity of 0.93 (95% CI, 0.87–0.97) and a specificity of 0.73 (95% CI, 0.67–0.78), while combined with HE4, it had a sensitivity of 0.90 (95% CI, 0.84–0.95) and a specificity of 0.81 (95% CI, 0.76–0.86). The IOTA ADNEX model combined with HE4 was better at improving the accuracy of the differential diagnosis between different OCs than the IOTA ADNEX model alone. A significant difference was found in separating borderline masses from Stage II–IV OC (p = 0.0257).

Conclusions

A combination of the IOTA ADNEX model and HE4 can improve the specificity of diagnosis of ovarian benign and malignant tumors and increase the sensitivity and effectiveness of the differential diagnosis of Stage II–IV OC and borderline tumors.

Diagnostic Models Combining Clinical Information, Ultrasound and Biochemical Markers for Ovarian Cancer: Cochrane Systematic Review and Meta-Analysis

Background: Ovarian cancer (OC) is a diagnostic challenge, with the majority diagnosed at late stages. Existing systematic reviews of diagnostic models either use inappropriate meta-analytic methods or do not conduct statistical comparisons of models or stratify test performance by menopausal status. Methods: We searched CENTRAL, MEDLINE, EMBASE, CINAHL, CDSR, DARE, Health Technology Assessment Database and SCI Science Citation Index, trials registers, conference proceedings from 1991 to June 2019. Cochrane collaboration review methods included QUADAS-2 quality assessment and meta-analysis using hierarchical modelling. RMI, ROMA or ADNEX at any test positivity threshold were investigated. Histology or clinical follow-up was the reference standard. We excluded screening studies, studies restricted to pregnancy, recurrent or metastatic OC. 2 × 2 diagnostic tables were extracted separately for pre- and post-menopausal women. Results: We included 58 studies (30,121 patients, 9061 cases of ovarian cancer). Prevalence of OC ranged from 16 to 55% in studies. For premenopausal women, ROMA at a threshold of 13.1 (+/−2) and ADNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at 200 (p < 0.0001) 77.8 (72.5, 82.4), 94.9 (92.5, 96.6), and 57.1% (50.6 to 63.4) but lower specificity (p < 0.002), 92.5 (90.0, 94.4), 84.3 (81.3, 86.8), and 78.2 (75.8, 80.4). For postmenopausal women, ROMA at a threshold of 27.7 (+/−2) and AdNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at a threshold of 200 (p < 0.001) 90.4 (87.4, 92.7), 97.6 (96.2, 98.5), and 78.7 (74.3, 82.5), specificity of ROMA was comparable, whilst ADneX was lower, 85.5 (81.3, 88.9), 81.3 (76.9, 85.0) (p = 0.155), compared to RMI 55.2 (51.2, 59.1) (p < 0.001). Conclusions: In pre-menopausal women, ROMA and ADNEX offer significantly higher sensitivity but significantly decreased specificity. In post-menopausal women, ROMA demonstrates significantly higher sensitivity and comparable specificity to RMI I, ADNEX has the highest sensitivity of all models, but with significantly reduced specificity. RMI I has poor sensitivity compared to ROMA or ADNEX. Choice between ROMA and ADNEX as a replacement test will depend on cost effectiveness and resource implications.

Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women

BACKGROUND

Ovarian cancer (OC) has the highest case fatality rate of all gynaecological cancers. Diagnostic delays are caused by non-specific symptoms. Existing systematic reviews have not comprehensively covered tests in current practice, not estimated accuracy separately in pre- and postmenopausal women, or used inappropriate meta-analytic methods.

OBJECTIVES

To establish the accuracy of combinations of menopausal status, ultrasound scan (USS) and biomarkers for the diagnosis of ovarian cancer in pre- and postmenopausal women and compare the accuracy of different test combinations.

SEARCH METHODS

We searched CENTRAL, MEDLINE (Ovid), Embase (Ovid), five other databases and three trial registries from 1991 to 2015 and MEDLINE (Ovid) and Embase (Ovid) form June 2015 to June 2019. We also searched conference proceedings from the European Society of Gynaecological Oncology, International Gynecologic Cancer Society, American Society of Clinical Oncology and Society of Gynecologic Oncology, ZETOC and Conference Proceedings Citation Index (Web of Knowledge). We searched reference lists of included studies and published systematic reviews.

SELECTION CRITERIA

We included cross-sectional diagnostic test accuracy studies evaluating single tests or comparing two or more tests, randomised trials comparing two or more tests, and studies validating multivariable models for the diagnosis of OC investigating test combinations, compared with a reference standard of histological confirmation or clinical follow-up in women with a pelvic mass (detected clinically or through USS) suspicious for OC.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and assessed quality using QUADAS-2. We used the bivariate hierarchical model to indirectly compare tests at commonly reported thresholds in pre- and postmenopausal women separately. We indirectly compared tests across all thresholds and estimated sensitivity at fixed specificities of 80% and 90% by fitting hierarchical summary receiver operating characteristic (HSROC) models in pre- and postmenopausal women separately.

MAIN RESULTS

We included 59 studies (32,059 women, 9545 cases of OC). Two tests evaluated the accuracy of a combination of menopausal status and USS findings (IOTA Logistic Regression Model 2 (LR2) and the Assessment of Different NEoplasias in the adneXa model (ADNEX)); one test evaluated the accuracy of a combination of menopausal status, USS findings and serum biomarker CA125 (Risk of Malignancy Index (RMI)); and one test evaluated the accuracy of a combination of menopausal status and two serum biomarkers (CA125 and HE4) (Risk of Ovarian Malignancy Algorithm (ROMA)). Most studies were at high or unclear risk of bias in participant, reference standard, and flow and timing domains. All studies were in hospital settings. Prevalence was 16% (RMI, ROMA), 22% (LR2) and 27% (ADNEX) in premenopausal women and 38% (RMI), 45% (ROMA), 52% (LR2) and 55% (ADNEX) in postmenopausal women. The prevalence of OC in the studies was considerably higher than would be expected in symptomatic women presenting in community-based settings, or in women referred from the community to hospital with a suspicion of OC. Studies were at high or unclear applicability because presenting features were not reported, or USS was performed by experienced ultrasonographers for RMI, LR2 and ADNEX. The higher sensitivity and lower specificity observed in postmenopausal compared to premenopausal women across all index tests and at all thresholds may reflect highly selected patient cohorts in the included studies. In premenopausal women, ROMA at a threshold of 13.1 (± 2), LR2 at a threshold to achieve a post-test probability of OC of 10% and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 77.4%, 95% CI 72.7% to 81.5%; LR2: 83.3%, 95% CI 74.7% to 89.5%; ADNEX: 95.5%, 95% CI 91.0% to 97.8%) compared to RMI (57.2%, 95% CI 50.3% to 63.8%). The specificity of ROMA and ADNEX were lower in premenopausal women (ROMA: 84.3%, 95% CI 81.2% to 87.0%; ADNEX: 77.8%, 95% CI 67.4% to 85.5%) compared to RMI 92.5% (95% CI 90.3% to 94.2%). The specificity of LR2 was comparable to RMI (90.4%, 95% CI 84.6% to 94.1%). In postmenopausal women, ROMA at a threshold of 27.7 (± 2), LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 90.3%, 95% CI 87.5% to 92.6%; LR2: 94.8%, 95% CI 92.3% to 96.6%; ADNEX: 97.6%, 95% CI 95.6% to 98.7%) compared to RMI (78.4%, 95% CI 74.6% to 81.7%). Specificity of ROMA at a threshold of 27.7 (± 2) (81.5, 95% CI 76.5% to 85.5%) was comparable to RMI (85.4%, 95% CI 82.0% to 88.2%), whereas for LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) specificity was lower (LR2: 60.6%, 95% CI 50.5% to 69.9%; ADNEX: 55.0%, 95% CI 42.8% to 66.6%).

AUTHORS' CONCLUSIONS

In specialist healthcare settings in both premenopausal and postmenopausal women, RMI has poor sensitivity. In premenopausal women, ROMA, LR2 and ADNEX offer better sensitivity (fewer missed cancers), but for ROMA and ADNEX this is off-set by a decrease in specificity and increase in false positives. In postmenopausal women, ROMA demonstrates a higher sensitivity and comparable specificity to RMI. ADNEX has the highest sensitivity in postmenopausal women, but reduced specificity. The prevalence of OC in included studies is representative of a highly selected referred population, rather than a population in whom referral is being considered. The comparative accuracy of tests observed here may not be transferable to non-specialist settings. Ultimately health systems need to balance accuracy and resource implications to identify the most suitable test.

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.

Diagnostic Accuracies of the Ultrasound and Magnetic Resonance Imaging ADNEX Scoring Systems For Ovarian Adnexal Mass: Systematic Review and Meta-Analysis

We conducted a meta-analysis of IOTA (international ovarian tumor analysis) ADNEX (Assessment of Different NEoplasias in the adneXa) as ultrasound system and MRI (magnetic resonance imaging) ADNEX scoring systems as MR system to assess their diagnostic test accuracy for differentiating benign from malignant adnexal masses of the ovary. We performed an electronic search for relevant publications in the English language up to February 2021 using PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science, and Google scholar databases and search engines. We computed the pooled sensitivity, pooled specificity, and summary receiver operating characteristics curve (SROC) using the statistical software STATA (Version 13, College Station, TX, StataCorp LP). Based on 11 studies using IOTA-ADNEX, we observed pooled sensitivity, specificity, area under curve, and diagnostic odds ratio were 96% (95% CI, 94% to 97%), 79% (95% CI, 70% to 86 %), 97% (95% CI, 95% to 98%), and 88 (95% CI, 43 to 180). Based on five studies using MR-ADNEX scoring system the pooled sensitivity, specificity, area under curve and diagnostic odds ratio were 91 % (95% CI, 87% to 94 %), 95% (95% CI, 92% to 97 %), 98% (95% CI, 96% to 99%), and 189 (95% CI, 90 to 396) respectively. Our meta-analysis results demonstrate that the MR-ADNEX scoring system had higher specificity however bit lower sensitivity compared to the IOTA-ADNEX scoring system for discriminating benign from malignant ovarian tumors.

Value of Assessment of Different Neoplasias in the Adnexa in the Differential Diagnosis of Malignant Ovarian Tumor and Benign Ovarian Tumor: A Meta-analysis

To evaluate the accuracy of the assessment of different neoplasias in the adnexa (ADNEX) model in the differential diagnosis of malignant and benign ovarian tumors, the optimal cutoff value and the accuracy in diagnosing ovarian tumors at different stages, PubMed, Web of Science and Cochrane Library databases were retrieved to search literature with per-patient analysis until publication of the last study in November 2021. STATA 14.1, Meta-Disc 1.4 and Revman software 5.3 were used in the performance of meta-analysis. To explore sources of heterogeneity, a subgroup analysis was conducted for the ADNEX model. The pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood, negative likelihood ratio and area under the summary receiver operating characteristic curve were 0.91 (95% confidence interval [CI]: 0.89-0.93), 0.84 (95% CI: 0.80-0.88), 55.55 (95% CI: 40.47-76.26), 5.71 (95% CI: 4.49-7.26), 0.10 (95% CI: 0.08-0.13) and 0.94 (95% CI: 0.92-0.96) in differentiating benign and malignant ovarian tumors, respectively. The area under the curve in identifying benign, borderline, stage I and stages II-IV were 0.93, 0.73, 0.27 and 0.92. The ADNEX model had high diagnostic performance was influential in the diagnosis of benign and stage II-IV ovarian tumors.

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DL_feature] or decision fusion [hereafter, DL_decision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DL_feature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DL_decision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DL_decision, DL_feature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.

Comparison of Ultrasound and Tumor Marker CA125 in Diagnosis of Adnexal Mass Malignancies

Background

CA125 is the most used tumor marker for ovarian cancer monitoring and diagnosis. This study aimed to evaluate the capacity to predict malignancy in women with adnexal tumors using CA125 measurement and ultrasound criteria before the pathological examination.

Materials and Methods

This observational diagnostic study was conducted on 300 patients with obvious diagnosis of adnexal mass consists of ovarian masses, fallopian tubes, and masses within the broad ligament referring to Alzahra and Beheshti Hospitals from 2018 to 2019. Ultrasound examinations were done before surgery and malignancy risk was investigated by the ADNEX criterion. Sensitivity, specificity, positive and negative likelihood ratio (likelihood ratio [LR]+ and LR-), and area under the curve (AUC) were calculated.

Results

From 284 patients, 260 masses were categorized in benign, 18 were in borderline, and 18 masses were malignant. The mean age of patients with malignant tumors was significantly higher than the others (P = 0.01). Differences in the level of CA-125 were not statistically significant (P = 0.78). Furthermore, the proportion of ascites in the malignant group (16.3%) was significantly higher than the others (P = 0.003). The AUC in ADNEX model (cutoff ≥9%) for differentiation of benign and malignant tumors was 0.75 (95% confidence interval [CI]: 0.69-0.80) with a sensitivity of 0.63 (95% CI: 0.41-0.81) and a specificity of 0.80 (95% CI: 0.74-0.84). Receiver operating characteristic analysis for CA-125 revealed that this variable is not capable for discrimination between benign and malignant tumors as the AUCs of the aforementioned variable were 0.60, 0.60, and 0.52 for the whole patients, premenopause, and postmenopause categories.

Conclusion

CA-125 marker, along with other ultrasound findings, can be more accurate in identifying the malignancy of the adnexa tumor.

Two-Step Strategy for Optimizing the Preoperative Classification of Adnexal Masses in a University Hospital, Using International Ovarian Tumor Analysis Models: Simple Rules and Assessment of Different NEoplasias in the adneXa Model

OBJECTIVES

To evaluate the performance of a two-step strategy compared with the International Ovarian Tumor Analysis (IOTA) - Assessment of Different NEoplasias in the adneXa (ADNEX) model for preoperative classification of adnexal masses.

METHODS

An ambispective diagnostic accuracy study based on ultrasound data collected at one university hospital between 2012 and 2018. Two ultrasonographers classified the adnexal masses using IOTA Simple Rules (first step). Not classifiable masses were evaluated using the IOTA ADNEX model (second step). Also, all masses were classified using the IOTA ADNEX model. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-), and receiver operating characteristic (ROC) curve were estimated. A P value of <.05 was used to determine statistical significance.

RESULTS

The study included 548 patients and 606 masses. Patients' median age was 41 years with an interquartile range between 32 and 51 years. In the first step, 89 (14%) masses were not classifiable. In the second step, 55 (61.8%) masses were classified as malignant. Furthermore, for the totality of 606 masses, the IOTA ADNEX model estimated the probability that 126 (20.8%) masses were malignant. The two-step strategy had a sensitivity, specificity, PPV, NPV, LR+, LR-, and ROC curve of 86.8%, 91.01%, 51.9%, 98.4%, 9.7, 0.1, and 0.889, respectively; compared to IOTA ADNEX model that had values of 91.8%, 87.16%, 44.4%, 99%, 7.1, 0.09, and 0.895, respectively.

CONCLUSION

The two-step strategy shows a similar diagnostic performance when compared to the IOTA ADNEX model. The IOTA ADNEX model involves only one step and can be more practical, and thus would be recommended to use.

A comparison of the diagnostic performance of the O-RADS, RMI4, IOTA LR2, and IOTA SR systems by senior and junior doctors

Purpose

This study compared the diagnostic performance of the Ovarian-Adnexal Reporting and Data System (O-RADS), the Risk of Malignancy Index 4 (RMI4), the International Ovarian of Tumor Analysis Logistic Regression Model 2 (IOTA LR2), and the IOTA Simple Rules (IOTA SR) in predicting the malignancy of adnexal masses (AMs).

Methods

This retrospective study included 575 women with AMs between 2017 and 2020. All clinical messages, ultrasound images, and pathological findings were collected. Two senior doctors (group I) and two junior doctors (group II) used the four systems to classify AMs. The postoperative pathological diagnosis was used as the gold standard to evaluate the diagnostic efficiency. A receiver operating characteristic curve was used to test the diagnostic performance. The interrater agreement between the two groups was tested using kappa values.

Results

Of all 592 AMs, 447 (75.5%) were benign, 123 (20.8%) were malignant, and 22 (3.7%) were borderline. The intergroup consistency test yielded kappa values of 0.71, 0.92, 0.68, and 0.77 for the O-RADS, RMI4, IOTA LR2, and IOTA SR, respectively. To predict malignant lesions, the areas under the curve of the O-RADS, RMI4, IOTA LR2, and IOTA SR systems were 0.90, 0.89, 0.90, and 0.86 for group I and 0.89, 0.87, 0.88, and 0.84 for group II, respectively. The O-RADS had the highest sensitivity (91.0% in group I and 84.8% in group II).

Conclusion

The four diagnostic systems could compensate for junior doctors’ inexperience in predicting malignant adnexal lesions. The O-RADS performed best and showed the highest sensitivity.

Significance of Pelvic Fluid Observed during Ovarian Cancer Screening with Transvaginal Sonogram

The primary objective was to examine the role of pelvic fluid observed during transvaginal ultrasonography (TVS) in identifying ovarian malignancy. A single-institution, observational study was conducted within the University of Kentucky Ovarian Cancer Screening trial from January 1987 to September 2019. We analyzed true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) groups for the presence of pelvic fluid during screening encounters. Measured outcomes were the presence and duration of fluid over successive screening encounters. Of the 48,925 women surveyed, 2001 (4.1%) had pelvic fluid present during a TVS exam. The odds ratio (OR) of detecting fluid in the comparison group (TN screen; OR = 1) significantly differed from that of the FP cases (benign pathology; OR: 13.4; 95% confidence interval (CI): 9.1–19.8), the TP cases with a low malignant potential (LMP; OR: 28; 95% CI: 26.5–29.5), TP ovarian cancer cases (OR: 50.4; 95% CI: 27.2–93.2), and FN ovarian cancer cases (OR: 59.3; 95% CI: 19.7–178.1). The mean duration that pelvic fluid was present for women with TN screens was 2.2 ± 0.05 encounters, lasting 38.7 ± 1.3 months. In an asymptomatic screening population, free fluid identified in TVS exams was more associated with ovarian malignancy than in the control group or benign ovarian tumors. While pelvic free fluid may not solely discriminate malignancy from non-malignancy, it appears to be clinically relevant and warrants thoughtful consideration.

Estimating the risk of malignancy of adnexal masses: validation of the ADNEX model in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China

Background

This study aims to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) the Assessment of Different NEoplasias in the adneXa (ADNEX) model in the preoperative diagnosis of adnexal masses in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China.

Methods

This was a single oncology centre, retrospective diagnostic accuracy study of 620 patients. All patients underwent surgery, and the histopathological diagnosis was used as a reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumours, borderline ovarian tumours (BOTs), stage I ovarian cancer (OC), stage II-IV OC and ovarian metastasis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumours into different histological types with and without cancer antigen 125 (CA 125) results.

Results

Of the 620 women, 402 (64.8%) had a benign ovarian tumour and 218 (35.2%) had a malignant ovarian tumour, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate benign and malignant adnexal masses was 0.97 (95% CI, 0.96–0.98). Performance was excellent for the discrimination between benign and stage II-IV OC and between benign and ovarian metastasis, with AUCs of 0.99 (95% CI, 0.99–1.00) and 0.99 (95% CI, 0.98–1.00), respectively. The model was less effective at distinguishing between BOT and stage I OC and between BOT and ovarian metastasis, with AUCs of 0.54 (95% CI, 0.45–0.64) and 0.66 (95% CI, 0.56–0.77), respectively. When including CA125 in the model, the performance in discriminating between stage II–IV OC and stage I OC and between stage II–IV OC ovarian metastasis was improved (AUC increased from 0.88 to 0.94, P = 0.01, and from 0.86 to 0.97, p = 0.01).

Conclusions

The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of nonexpert ultrasonographers with limited experience in China. In classifying different subtypes of ovarian cancers, the model has difficulty differentiating BOTs from stage I OC and BOTs from ovarian metastases.

Evaluation of the Diagnostic Value of the Ultrasound ADNEX Model for Benign and Malignant Ovarian Tumors

Objective

To investigate the diagnostic performance of the ADNEX model in the International Ovarian Tumor Analysis diagnostic models for ovarian tumors and further explore its application value in the staging of ovarian tumors.

Methods

A total of 224 patients who underwent ultrasound for evaluation of adnexal masses and were treated surgically owing to adnexal masses from January 2018 to June 2020 in our hospital were selected for research on the diagnostic accuracy of the ADNEX model. The clinical information and ultrasonographic findings of the patients were collected, and the pathological diagnosis was taken as the gold standard. According to the ADNEX model, the ovarian tumors were divided into five subtypes: benign and borderline, stage I, stage II–IV, and metastatic cancer. The sensitivity, specificity, positive predictive value, negative predictive value, diagnostic odds ratio, and area under the receiver operating characteristics curve (AUC) of the ADNEX model were calculated.

Results

Of the 224 patients, 119 (53.1%) developed benign tumors and 105 (46.9%) had malignant tumors. When the cut-off value for malignancy risk was 10%, the ADNEX model including CA 125 achieved a sensitivity of 94.3% (95% CI: 88.0–97.9%), specificity of 74.0% (95% CI: 65.1–81.6%), positive predictive value of 76.2% (95% CI: 70.2–81.3%), negative predictive value of 93.6% (95% CI: 87.0–97.0%), diagnostic odds ratio of 45.25, and an AUC of 0.94 (95% CI: 0.90–0.97) for differentiating between benign and malignant ovarian tumors. The AUC in the model excluding CA 125 was 0.93 (95% CI: 0.89–0.96), but the difference was not statistically significant (P=0.20). The accuracy of the ADNEX model for the diagnosis of ovarian tumors of all subtypes exceeds 80% when CA 125 measurements were included in the application, but the sensitivity for diagnosing borderline, stage I, and metastatic ovarian tumors was only 60.0% (95% CI:36.1–80.9%), 28.6% (95% CI:8.4–58.1%) and 45.5% (95% CI:16.7–76.6%).

Conclusion

The ADNEX model shows good diagnostic performance in differentiating between benign and malignant ovarian tumors. The model has a certain clinical value in the diagnosis of all subtypes of ovarian tumors, but the sensitivity is unsatisfactory for the diagnosis of borderline, stage I, and metastatic ovarian tumors and needs to be verified.

[Adnexal Masses: Clinical Application of Multiparametric MR Imaging & O-RADS MRI]

Incidental adnexal masses considered indeterminate for malignancy are commonly observed on ultrasonography. Multiparametric MRI is the imaging modality of choice for the evaluation of sonographically indeterminate adnexal masses. Conventional MRI enables a confident pathologic diagnosis of various benign lesions due to accurate tissue characterization of fat, blood, fibrous tissue, and solid components. Additionally, functional imaging sequences, including perfusion- and diffusion-weighted imaging, improve the diagnostic efficacy of conventional MRI in differentiating benign from malignant adnexal masses. The ovarian-adnexal reporting and data system (O-RADS) MRI was recently designed to provide consistent interpretations in assigning risk of malignancy to ovarian and other adnexal masses, and to provide a management recommendation for each risk category. In this review, we describe the clinical application of multiparametric MRI for the evaluation of adnexal masses and introduce the O-RADS MRI risk stratification system.

Factors Influencing the Discordancy Between Intraoperative Frozen Sections and Final Paraffin Pathologies in Ovarian Tumors

Aim

To retrospectively investigate the pre-operative clinical factors and ultrasonographic features that influence the accuracy of the intraoperative frozen section (IFS) of ovarian tumors.

Patients and methods

Women with ovarian tumors that underwent IFS in one tertiary medical center were recruited from January 2010 to December 2018. Demographic and clinical data of these women were retrieved from medical records in the hospital's centralized database.

Results

A total of 903 ovarian tumors were enrolled, including 237 (26.2%) benign, 150 (16.6%) borderline tumor, and 516 (57.2%) malignant. The overall accuracy of IFS among all specimens was 89.9%. The sensitivities of IFS in diagnosing borderline tumors (82.0%) and malignant tumors (88.2%) were lower than in diagnosing benign tumors (98.7%, p <0.001, Z-test). The specificity of diagnosing malignant tumors (99.7%) was significantly higher than that of diagnosing benign tumors (94.7%, p <0.001, Z-test). The group with discordant IFS and final paraffin pathology (FPP) had younger age (47.2 ± 14.0 vs. 51.5 ± 11.8 years, p = 0.013, Mann-Whitney U test), and higher percentage of early-stage disease (85.2% vs. 65.1%, p = 0.001, chi-square test) and mucinous (39.3% vs. 3.3%) and endometrioid histologic types (34.4% vs. 20.2%) than the concordant group (all by chi-square test). Menopause (OR 0.34, 95% CI 0.15-0.76, p = 0.009), multicystic tumor in ultrasound (OR 2.14, 95% CI 1.14-4.01, p = 0.018), and ascites existence (OR 0.33, 95% CI 0.14-0.82, p = 0.016) were factors related to the discordant IFS by multivariate analysis.

Conclusions

IFS has good accuracy in the diagnosis of ovarian tumors. We recommend more frozen tissue sampling for sonographic multicystic tumors in premenopausal women to improve the accuracy of IFS.

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.

Diagnostic Accuracy of the ADNEX Model for Ovarian Cancer at the 15% Cut-Off Value: A Systematic Review and Meta-Analysis

Objectives

To evaluate the diagnostic accuracy of the ADNEX model for ovarian cancer at the 15% cut-off value.

Methods

Studies on the identified diagnosis of the ADNEX model for ovarian cancer published in PubMed, Embase, the Cochrane Library and Web of Science databases from January 1st, 2014 to February 20th, 2021 were searched. Two researchers independently screened the retrieved studies and extracted the basic features and parameter data. The quality of the eligible studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2, and the result was summarized by Review Manager 5.3. Meta-Disc 1.4 and STATA 16.0 were used in statistical analysis. Heterogeneity of this meta-analysis was calculated. Meta-regression was performed to investigate the potential sources of heterogeneity. Sensitivity analysis and Deek's funnel plot analysis were conducted to evaluate the stability and publication bias, respectively.

Results

280 studies were initially retrieved through the search strategy, and 10 eligible studies were ultimately included. The random-effects model was selected for data synthesis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the summary receiver operating characteristic curve were 0.92 (95% CI: 0.89-0.94), 0.82 (95% CI: 0.78-0.86), 5.2 (95% CI: 4.1-6.4), 0.10 (95% CI: 0.07-0.13), 54.0 (95% CI: 37.0-77.0) and 0.95 (95% CI: 0.91-0.95). Meta-regression based on study design, country, enrollment and blind method was not statistically significant. This meta-analysis was stable with no obvious publication bias.

Conclusions

The ADNEX model at the 15% cut-off had high diagnostic accuracy in identifying ovarian cancer.

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.

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.

Comparison of the Diagnostic Performances of Ultrasound-Based Models for Predicting Malignancy in Patients With Adnexal Masses

Aim

This study aimed to compare different ultrasound-based International Ovarian Tumor Analysis (IOTA) prediction models, namely, the Simple Rules (SRs) the Assessment of Different NEoplasias in the adneXa (ADNEX) models, and the Risk of Malignancy Index (RMI), for the pre-operative diagnosis of adnexal mass.

Methods

This single-centre diagnostic accuracy study involved 486 patients. All ultrasound examinations were analyzed and the prediction models were applied. Pathology was the clinical reference standard. The diagnostic performances of prediction models were measured by evaluating receiver-operating characteristic curves, sensitivities, specificities, positive and negative predictive values, positive and negative likelihood ratios, and diagnostic odds ratios.

Results

To discriminate benign and malignant tumors, areas under the ROC curves (AUCs) for ADNEX models were 0.94 (95% CI: 0.92–0.96) with CA125 and 0.94 (95% CI: 0.91–0.96) without CA125, which were significantly higher than the AUCs for RMI I-III: 0.87 (95% CI: 0.83–0.90), 0.83 (95% CI: 0.80–0.86), and 0.82 (95% CI: 0.78–0.86), (all P < 0.0001). At a cut-off of 10%, the ADNEX model with CA125 had the highest sensitivity (0.93; 95% CI: 0.87–0.97) compared with the other models. The SRs model achieved a sensitivity of 0.93 (95% CI: 0.86–0.97) and a specificity of 0.86 (95% CI: 0.82–0.89) when inconclusive diagnoses (11.7%) were classified as malignant.

Conclusion

ADNEX and SRs models were excellent at characterising adnexal masses which were superior to the RMI in Chinese patients.

Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses

OBJECTIVE

To assess the diagnostic performance and inter-observer agreement of the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US).

METHODS

From January 2016 to December 2018 a total of 1054 adnexal lesions in 1035 patients with pathologic results from two hospitals were retrospectively included. Each lesion was assigned to an O-RADS US category according to the criteria. Kappa (κ) statistics were applied to assess inter-observer agreement between a less experienced and an expert radiologist.

RESULTS

Of the 1054 adnexal lesions, 750 were benign and 304 were malignant. The malignancy rates of O-RADS 5, O-RADS 4, O-RADS 3, and O-RADS 2 lesions were 89.57%, 34.46%, 1.10%, and 0.45% respectively. Area under the receiver operating characteristic curve was 0.960 (95% CI, 0.947-0.971). The optimal cutoff value for predicting malignancy was >O-RADS 3 with a sensitivity and specificity of 98.7% (95% CI, 0.964-0.996) and 83.2% (95% CI, 0.802-0.858) respectively. When sub-classifying multilocular cysts and smooth solid lesions in O-RADS 4 lesions as O-RADS 4a lesions and the rest cystic lesions with solid components as O-RADS 4b lesions, the malignancy rate were 17.02% and 42.57% respectively, which showed better risk stratification (P < 0.001). The inter-observer agreement between a less-experienced and an expert radiologist of O-RADS categorization was good (κ = 0.714).

CONCLUSIONS

The ACR O-RADS US provides effective malignancy risk stratification for adnexal lesions with high reliability for radiologists with different experience. Sub-grouping of O-RADS 4 lesions into two groups facilitated better stratification of the intermediate risk.

Preoperative Evaluation of the ADNEX Model for the Prediction of the Ovarian Cancer Risk of Adnexal Masses at Siriraj Hospital

INTRODUCTION

Distinguishing benign adnexal masses from malignant tumors plays an important role in preoperative planning and improving patients' survival rates. The International Ovarian Tumor Analysis (IOTA) group developed a model termed the Assessment of Different NEoplasias in the adneXa (ADNEX).

OBJECTIVE

Our objective was to evaluate the performance of the ADNEX model in distinguishing between benign and malignant tumors at a cutoff value of 10%.

METHODS

This was a prospective diagnostic study. 357 patients with an adnexal mass who were scheduled for surgery at Siriraj Hospital were included from May 1, 2018, to May 30, 2019. All patients were undergoing ultrasonography, and serum CA125 was measured. Data were calculated by the ADNEX model via an IOTA ADNEX calculator.

RESULTS

Of the 357 patients, 296 had benign tumors and 61 had malignant tumors. The area under the receiver operating characteristic curve for using the ADNEX model was 0.975 (95% confidence interval, 0.953-0.988). At a 10% cutoff, the sensitivity was 98.4% and specificity was 87.2%. The best cutoff value was at 16.6% in our population.

CONCLUSIONS

The performance of the ADNEX model in differentiating benign and malignant tumors was excellent.

Assessment of different NEoplasias in the adneXa model for differentiation of benign and malignant adnexal masses in Korean women

OBJECTIVE

Ultrasonographic differential diagnosis of ovarian tumors is important for appropriate management. We conducted study to compare the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model with a subjective assessment (SA) in differentiating between benign and malignant adnexal masses in Korean women.

METHODS

A total of 353 patients who underwent adnexal surgery with abnormal pelvic ultrasonographic findings from August 2016 to August 2017 were included in study. The presumptive diagnosis of adnexal malignancy was determined by both SA and the ADNEX model to be >10% calculated risk of malignancy. The area under the curve (AUC) comparison between the SA and ADNEX models was performed using DeLong's method.

RESULTS

340 patients with benign tumors and 13 with malignant adnexal tumors among 292 (82.72%) premenopausal and 61 (17.28%) postmenopausal women were included. The AUCs of SA and the ADNEX model for discrimination between benign and malignant tumors were 0.79 and 0.92, respectively (P=0.10). The sensitivity and specificity of SA and the ADNEX model were 83.5% and 97.0%, and 90.0% and 82.0%, respectively. Comparison of the ADNEX model regarding menopausal status revealed that the predictability was not different. The AUCs of SA and the ADNEX model in premenopausal women were 0.74 and 0.89, respectively (P=0.12). The AUCs of SA and the ADNEX model in postmenopausal women were 0.86 and 0.94, respectively (P=0.60).

CONCLUSION

The ADNEX model offers excellent discrimination between benign and malignant ovarian tumors with similar sensitivity and specificity to SA in both premenopausal and postmenopausal Korean women.

Predicting Malignancy in Adnexal Masses by the International Ovarian Tumor Analysis-Simple Rules

Background:

Accurate prediction of adnexal tumors preoperatively is critical for optimal management of ovarian cancers. The International Ovarian Tumor Analysis Algorithms (IOTA) is a newer tool to characterize adnexal masses as benign or malignant.

Objective:

This study is aimed to predict malignancy in adnexal masses and differentiates benign from malignant, applying the sonography features of simple rules given by IOTA.

Methodology:

A prospective study was carried out at AIIMS Jodhpur for 1½ years. Women presenting with adnexal masses planned for surgery were recruited. Ultrasonography-transabdominal combined with transvaginal was done, and pelvic masses were characterized using IOTA simple rules. Patients underwent their planned surgery. Histopathology is considered the gold standard and was compared with the IOTA simple rules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

Results:

One hundred and seventy-four women were included in the study, of which the majority (82.75%) were benign, the rest being frankly malignant or borderline cancer. The sensitivity of IOTA is 96.6%, specificity of 92.3%, PPV of 72.5%, NPV of 99.2%, where indeterminate cases were considered malignant.

Conclusion:

IOTA simple rule is an effective tool for identifying malignant adnexal masses. It also suggests that IOTA-simple rules can be used as a diagnostic criterion for differentiating adnexal masses into benign and malignant on an out-patient department basis.

Multimodality imaging review of complex pelvic lesions in female pelvis

Complex pelvic lesions can originate from various anatomical structures in the pelvis and pose a diagnostic dilemma due to a wide range of possible diagnoses. Accurate characterisation of these lesions would often require an algorithmic approach, which incorporates clinical findings, sequential use of multiple imaging modalities and a multiparametric approach. This approach usually aims at identifying key imaging features, which aid in anatomical localisation, morphology and tissue characterisation. There have been various attempts to standardise the lexicon used for describing adnexal masses in female patients; stratify their risk of cancer and suggest appropriate next steps in the management pathway. Through this review, we extend this approach to complex pelvic masses in female pelvis in general and will focus on optimal use of different imaging modalities to arrive at definitive diagnosis or meaningful differential diagnosis. We will also discuss potential pitfalls of imaging diagnosis and common mimics.

Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study

OBJECTIVE

To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively.

DESIGN

Multicentre cohort study.

SETTING

36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre.

PARTICIPANTS

Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up.

MAIN OUTCOME MEASURES

Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain.

RESULTS

The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125.

CONCLUSIONS

Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01698632.

Surgery of Benign Ovarian Masses by a Gynecological Cancer Surgeon: A Cohort Study in a Tertiary Cancer Centre

Objectives This study aimed to evaluate diagnostic performance in characterising ovarian masses by our gynaecological oncology multidisciplinary team meeting (MDM). Surgical outcome and overall impact on patients and healthcare service were also assessed. Methods This was a prospective cohort study of all women with adnexal masses presenting to the gynaecological oncology MDM at a central London tertiary cancer centre between February 2017 and February 2018. The multidisciplinary team (MDT) outcome, imaging details, subjective opinion, tumour markers, surgical details, and final histological diagnosis were collected. Diagnostic performance was also determined. Results There were 200 eligible patients in the study period. MDM imaging review demonstrated a sensitivity of 98.4% (95% CI: 94.3% to 99.8%) and a specificity of 52% (95% CI: 40.2% to 63.7%). Thirty-five cases were false positive, either presumed invasive cancers (51%) or borderline tumours (49%). The most common histological types were serous (37%) and mucinous (31%) cystadenomas. A retrospective application of the International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model suggests a potential reduction in false-positive rates (17%). Among the false-positive cases, there was no postoperative (90 days) mortality and postoperative morbidity was 14% with only grade 2 (CD2) complications according to Clavien and Dindo's CD classification. Conclusion An MDT has high sensitivity but low specificity when characterising ovarian masses referred with possible ovarian cancer to the tertiary centre. False-positive values in ovarian cancers are an important indicator of over-treatment. More research is required to assess other methods, such as the IOTA ADNEX model, to reduce the false-positive value.

Validation of IOTA-ADNEX Model in Discriminating Characteristics of Adnexal Masses: A Comparison with Subjective Assessment

(1) Background: The aim of this study is to compare the IOTA-ADNEX (international ovarian tumor analysis–assessment of different neoplasias in the adnexa) model with gynecologic experts in differentiating ovarian diseases. (2) Methods: All participants in this prospective study underwent ultrasonography (US) equipped with the IOTA-ADNEX^TM model and subjective assessment by a sonographic expert. Receiver operating characteristic (ROC) curves were also generated to compare overall accuracies. The optimal cut-off value of the ADNEX model for excluding benign diseases was calculated. (3) Results: Fifty-nine participants were eligible: 54 and 5 underwent surgery and follow-up computed tomography (CT), respectively. Benign and malignant diseases were confirmed in 49 (83.1%) and 10 (16.9%) participants, respectively. The specificity of the ADNEX model was 0.816 (95% confidence interval (CI): 0.680–0.912) in all participants and 0.795 (95% CI, 0.647–0.902) in the surgical group. The area under the ROC curve of the ADNEX model (0.924) was not significantly different from that of subjective assessment (0.953 in all participants, 0.951 in the surgical group; p = 0.391 in all participants, p = 0.407 in the surgical group). The optimal cut-off point using the ADNEX model was 47.3%, with a specificity of 0.977 (95% CI: 0.880–0.999). (4) Conclusions: The IOTA-ADNEX model is equal to gynecologic US experts in excluding benign ovarian tumors. Subsequently, being familiar with this US software may help gynecologic beginners to reduce unnecessary surgery.

Ultrasound features of immature ovarian teratomas: Case series and review of literature

INTRODUCTION

Immature ovarian teratomas are rare but account for 10-20% of ovarian cancers in women under the age of 20 years. This study aimed to characterise immature ovarian teratomas using grey-scale and Doppler ultrasonography and review the literature to refine the diagnosis of immature ovarian teratomas.

METHODS

Patients with a confirmed histological diagnosis of immature ovarian teratoma from years 2006-2018, who had undergone a transvaginal ultrasound at two large teaching hospitals, were identified. The imaging was retrieved from the centres clinical databases. Ultrasound scans were performed by experienced ultrasound examiners and described according to International Ovarian Tumour Analysis criteria.

RESULTS

Eight patients were identified in total with a mean age of 26 years (range 13-35). Half of the patients had a past history of a mature ovarian teratoma (3 ipsilateral, 1 contralateral). The cysts were generally large (median 115 mm), fast growing unilateral lesions with a single, peripheral predominantly solid component arising from the cyst wall. The solid component was hyperechoic with multiple foci of fibrosis and numerous small cysts. The cystic component typically formed less than 75% of the lesion and the cyst fluid was of low-level echogenicity. Subjective assessment of vascularity of the solid part of the tumours varied between scores of 1 and 2. Tumour markers showed a raised serum a-fetoprotein level in 42% of these patients.

CONCLUSION

Although there were no ultrasound features that were pathognomonic of immature teratoma, the diagnosis should be suspected in a young woman with a large ovarian cyst with a fibrotic, microcystic solid component, particularly if she has a past history of a dermoid cyst.

Scoring systems for the evaluation of adnexal masses nature: current knowledge and clinical applications

Adnexal masses are a common finding in women, with 20% of them developing at least one pelvic mass during their lifetime. There are more than 30 different subtypes of adnexal tumours, with multiple different subcategories, and the correct characterisation of the pelvic masses is of paramount importance to guide the correct management. On that basis, different algorithms and scoring systems have been developed to guide the clinical assessment. The first scoring system implemented into the clinical practice was the Risk of Malignancy Index, which combines ultrasound evaluation, menopausal status, and serum CA-125 levels. Today, current guidelines regarding female patients with adnexal masses include the application of International Ovarian Tumours Analysis simple rules, logistic regression model 1 (LR1) and LR2, OVERA, cancer ovarii non-invasive assessment of treating strategy, and assessment of Different Neoplasias in the adnexa. In this scenario, the choice of the scoring system for the discrimination between benign and malignant ovarian tumours can be complex when approaching patients with adnexal masses. This review aims to summarise the available evidence regarding the different scoring systems to provide a complete overview of the topic.

The ADNEX model to triage adnexal masses: An external validation study and comparison with the IOTA two-step strategy and subjective assessment by an experienced ultrasound operator

OBJECTIVES

The ADNEX (Assessment of Different NEoplasias in the adneXa) model was developed using parameters collected by experienced (level III) ultrasound examiners. Our primary aim was to externally validate the ADNEX model. Then, the discriminatory performance of ADNEX was compared with the two-step strategy and subjective assessment by an experienced ultrasound operator.

METHODS

Between February 2013 and January 2017, all patients who were scheduled for surgery for an adnexal mass at the Sant'Anna Hospital in Turin were enrolled in this study. Preoperative transvaginal sonography was performed, and the two-step strategy was applied for triage of the adnexal mass. Two ultrasound examiners, IOTA certified, applied the ADNEX model to all the collected masses based on the ultrasound reports. Finally, an experienced operator assigned the subjective assessment based on recorded ultrasound images. The discrimination and calibration performance of ADNEX were evaluated. The AUC was calculated for the basic discrimination between benign and malignant tumours. In addition, AUCs were computed for each pair of tumour types using the conditional risk method.

RESULTS

A total of 577 patients were included in the analysis: the overall prevalence of malignancy was 25 %. With ADNEX, the AUC to differentiate between benign and malignant masses was 0.9111 (95 % CI 0. 8788-0.9389). At risk cut-offs of 1%, 10 % and 30 %, sensitivities were 100 %, 89.6 % and 79.2 %, respectively, and specificities were 2.8 %, 76.2 % and 89.6 %, respectively. Discrimination between benign and stage II-IV tumours was good (AUC 0.935). The model had the most difficulties discriminating between borderline and stage I tumours (AUC 0.666), and between stages II-IV invasive and secondary metastatic tumours (AUC 0.736). The polytomous discrimination index (PDI) was 0.61 for ADNEX, whereas PDI for random performance would be 0.25. ADNEX proved to be equally or more accurate than the subjective assessment or the two-step strategy in the discrimination between benign and malignant adnexal masses.

CONCLUSIONS

the ADNEX model could probably be successfully applied when an expert examiner is not available and, therefore both a subjective assessment and the two-step strategy cannot be performed.

Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) Score for Risk Stratification of Sonographically Indeterminate Adnexal Masses

IMPORTANCE

Approximately one-quarter of adnexal masses detected at ultrasonography are indeterminate for benignity or malignancy, posing a substantial clinical dilemma.

OBJECTIVE

To validate the accuracy of a 5-point Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) score for risk stratification of adnexal masses.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study was conducted between March 1, 2013, and March 31, 2016. Among patients undergoing expectant management, 2-year follow-up data were completed by March 31, 2018. A routine pelvic MRI was performed among consecutive patients referred to characterize a sonographically indeterminate adnexal mass according to routine diagnostic practice at 15 referral centers. The MRI score was prospectively applied by 2 onsite readers and by 1 reader masked to clinical and ultrasonographic data. Data analysis was conducted between April and November 2018.

MAIN OUTCOMES AND MEASURES

The primary end point was the joint analysis of true-negative and false-negative rates according to the MRI score compared with the reference standard (ie, histology or 2-year follow-up).

RESULTS

A total of 1340 women (mean [range] age, 49 [18-96] years) were enrolled. Of 1194 evaluable women, 1130 (94.6%) had a pelvic mass on MRI with a reference standard (surgery, 768 [67.9%]; 2-year follow-up, 362 [32.1%]). A total of 203 patients (18.0%) had at least 1 malignant adnexal or nonadnexal pelvic mass. No invasive cancer was assigned a score of 2. Positive likelihood ratios were 0.01 for score 2, 0.27 for score 3, 4.42 for score 4, and 38.81 for score 5. Area under the receiver operating characteristic curve was 0.961 (95% CI, 0.948-0.971) among experienced readers, with a sensitivity of 0.93 (95% CI, 0.89-0.96; 189 of 203 patients) and a specificity of 0.91 (95% CI, 0.89-0.93; 848 of 927 patients). There was good interrater agreement among both experienced and junior readers (κ = 0.784; 95% CI, 0.743-0824). Of 580 of 1130 women (51.3%) with a mass on MRI and no specific gynecological symptoms, 362 (62.4%) underwent surgery. Of them, 244 (67.4%) had benign lesions and a score of 3 or less. The MRI score correctly reclassified the mass origin as nonadnexal with a sensitivity of 0.99 (95% CI, 0.98-0.99; 1360 of 1372 patients) and a specificity of 0.78 (95% CI, 0.71-0.85; 102 of 130 patients).

CONCLUSIONS AND RELEVANCE

In this study, the O-RADS MRI score was accurate when stratifying the risk of malignancy in adnexal masses.

Performance of IOTA ADNEX model in evaluating adnexal masses in a gynecological oncology center in China

OBJECTIVE

To evaluate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model in the preoperative diagnosis of adnexal masses using data from a gynecological oncology center in China.

METHODS

This was a single-center, retrospective diagnostic accuracy study based on ultrasound data collected prospectively, between May and December 2017, from 278 patients with at least one adnexal (ovarian, paraovarian or tubal) mass. Clinical and pathologic information, serum CA 125 level and ultrasonographic findings were collected. All patients underwent surgery and the histopathological diagnosis was used as reference standard. The final diagnosis was classified into five tumor types according to the ADNEX model: benign ovarian tumor, borderline ovarian tumor (BOT), Stage-I ovarian cancer (OC), Stages-II-IV OC and ovarian metastasis. Receiver-operating characteristics (ROC) curve analysis was used to evaluate the diagnostic accuracy of the ADNEX model, with and without inclusion of CA 125 level in the model.

RESULTS

Of the 278 women included, 203 (73.0%) had a benign ovarian tumor and 75 (27.0%) had a malignant ovarian tumor, including 18 (6.5%) with BOT, 17 (6.1%) with Stage-I OC, 32 (11.5%) with Stages-II-IV OC and eight (2.9%) with ovarian metastasis. The performance of the IOTA ADNEX model was good for discriminating between benign and malignant tumors, with an area under the ROC curve (AUC) of 0.94 (95% CI, 0.91-0.97) when CA 125 was included in the model and AUC of 0.93 (95% CI, 0.90-0.96) without CA 125. The AUC values of the model including CA 125 ranged between 0.61 and 0.99 for distinguishing between the different types of tumor, and it showed excellent performance in discriminating between a benign ovarian tumor and Stages-II-IV OC, with an AUC of 0.99 (95% CI, 0.97-1.00). The performance of the model was less effective at distinguishing between BOT and Stage-I OC and between Stages-II-IV OC and ovarian metastasis, with AUC values of 0.61 (95% CI, 0.43-0.77) and 0.78 (95% CI, 0.62-0.90), respectively. Although inclusion of CA 125 did not alter the performance of the ADNEX model in discriminating between benign and malignant lesions (AUC of 0.94 and 0.93 with and without CA 125 level, respectively; P = 0.54), the inclusion of CA 125 in the model improved its performance in discriminating between Stage-I OC and Stages-II-IV OC (AUC increased from 0.81 to 0.92; P = 0.04) and between Stages-II-IV OC and metastatic cancer (AUC increased from 0.58 to 0.78; P = 0.01).

CONCLUSIONS

The IOTA ADNEX model showed good to excellent performance in distinguishing between benign and malignant adnexal masses and between the different types of ovarian tumor in a Chinese setting. Based on our findings, the ADNEX model has high value in clinical practice and can aid in the preoperative diagnosis of patients with an adnexal mass. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.