Jeroen Kaijser

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study.

Objectives To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. Design Observational diagnostic study using prospectively collected clinical and ultrasound data. Setting 24 ultrasound centres in 10 countries. Participants Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. Main outcome measures Histological classification and surgical staging of the mass. Results The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. Conclusions The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.

Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis

BACKGROUND Characterizing ovarian pathology is fundamental to optimizing management in both pre- and post-menopausal women. Inappropriate referral to oncology services can lead to unnecessary surgery or overly radical interventions compromising fertility in young women, whilst the consequences of failing to recognize cancer significantly impact on prognosis. By reflecting on recent developments of new diagnostic tests for preoperative identification of malignant disease in women with adnexal masses, we aimed to update a previous systematic review and meta-analysis. METHODS An extended search was performed in MEDLINE (PubMed) and EMBASE (OvidSp) from March 2008 to October 2013. Eligible studies provided information on diagnostic test performance of models, designed to predict ovarian cancer in a preoperative setting, that contained at least two variables. Study selection and extraction of study characteristics, types of bias, and test performance was performed independently by two reviewers. Quality was assessed using a modified version of the QUADAS assessment tool. A bivariate hierarchical random effects model was used to produce summary estimates of sensitivity and specificity with 95% confidence intervals or plot summary ROC curves for all models considered. RESULTS Our extended search identified a total of 1542 new primary articles. In total, 195 studies were eligible for qualitative data synthesis, and 96 validation studies reporting on 19 different prediction models met the predefined criteria for quantitative data synthesis. These models were tested on 26 438 adnexal masses, including 7199 (27%) malignant and 19 239 (73%) benign masses. The Risk of Malignancy Index (RMI) was the most frequently validated model. The logistic regression model LR2 with a risk cut-off of 10% and Simple Rules (SR), both developed by the International Ovarian Tumor Analysis (IOTA) study, performed better than all other included models with a pooled sensitivity and specificity, respectively, of 0.92 [95% CI 0.88-0.95] and 0.83 [95% CI 0.77-0.88] for LR2 and 0.93 [95% CI 0.89-0.95] and 0.81 [95% CI 0.76-0.85] for SR. A meta-analysis of centre-specific results stratified for menopausal status of two multicentre cohorts comparing LR2, SR and RMI-1 (using a cut-off of 200) showed a pooled sensitivity and specificity in premenopausal women for LR2 of 0.85 [95% CI 0.75-0.91] and 0.91 [95% CI 0.83-0.96] compared with 0.93 [95% CI 0.84-0.97] and 0.83 [95% CI 0.73-0.90] for SR and 0.44 [95% CI 0.28-0.62] and 0.95 [95% CI 0.90-0.97] for RMI-1. In post-menopausal women, sensitivity and specificity of LR2, SR and RMI-1 were 0.94 [95% CI 0.89-0.97] and 0.70 [95% CI 0.62-0.77], 0.93 [95% CI 0.88-0.96] and 0.76 [95% CI 0.69-0.82], and 0.79 [95% CI 0.72-0.85] and 0.90 [95% CI 0.84-0.94], respectively. CONCLUSIONS An evidence-based approach to the preoperative characterization of any adnexal mass should incorporate the use of IOTA Simple Rules or the LR2 model, particularly for women of reproductive age.

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies

In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized the approach to the ultrasound description of adnexal pathology. A prospectively collected large database enabled previously developed prediction models like the risk of malignancy index (RMI) to be tested and novel prediction models to be developed and externally validated in order to determine the optimal approach to characterize adnexal pathology preoperatively. The main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Their test performance almost matches subjective assessment by experienced examiners, which is accepted to be the best way to classify adnexal masses before surgery. A two‐step strategy using the IOTA simple rules supplemented with subjective assessment of ultrasound findings when the rules do not apply, also reached excellent diagnostic performance (sensitivity 90%, specificity 93%) and misclassified fewer malignancies than did the RMI. An evidence‐based approach to the preoperative characterization of ovarian and other adnexal masses should include the use of LR1, LR2 or IOTA simple rules and subjective assessment by an experienced examiner. Copyright

Multicentre external validation of IOTA prediction models and RMI by operators with varied training

Background:Correct characterisation of ovarian tumours is critical to optimise patient care. The purpose of this study is to evaluate the diagnostic performance of the International Ovarian Tumour Analysis (IOTA) logistic regression model (LR2), ultrasound Simple Rules (SR), the Risk of Malignancy Index (RMI) and subjective assessment (SA) for preoperative characterisation of adnexal masses, when ultrasonography is performed by examiners with different background training and experience.Methods:A 2-year prospective multicentre cross-sectional study. Thirty-five level II ultrasound examiners contributed in three UK hospitals. Transvaginal ultrasonography was performed using a standardised approach. The final outcome was the surgical findings and histological diagnosis. To characterise the adnexal masses, the six-variable prediction model (LR2) with a cutoff of 0.1, the RMI with cutoff of 200, ten SR (five rules for malignancy and five rules for benignity) and SA were applied. The area under the curves (AUCs) for performance of LR2 and RMI were calculated. Diagnostic performance measures for all models assessed were sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−), and the diagnostic odds ratio (DOR).Results:Nine-hundred and sixty-two women with adnexal masses underwent transvaginal ultrasonography, whereas 255 had surgery. Prevalence of malignancy was 29% (49 primary invasive epithelial ovarian cancers, 18 borderline ovarian tumours, and 7 metastatic tumours). The AUCs for LR2 and RMI for all masses were 0.94 (95% confidence interval (CI): 0.89–0.97) and 0.90 (95% CI: 0.83–0.94), respectively. In premenopausal women, LR2−RMI difference was 0.09 (95% CI: 0.03–0.15) compared with −0.02 (95% CI: −0.08 to 0.04) in postmenopausal women. For all masses, the DORs for LR2, RMI, SR+SA (using SA when SR inapplicable), SR+MA (assuming malignancy when SR inapplicable), and SA were 62 (95% CI: 27–142), 43 (95% CI: 19–97), 109 (95% CI: 44–274), 66 (95% CI: 27–158), and 70 (95% CI: 30–163), respectively.Conclusion:Overall, the test performance of IOTA prediction models and rules as well as the RMI was maintained in examiners with varying levels of training and experience.

Strategies to diagnose ovarian cancer: new evidence from phase 3 of the multicentre international IOTA study

Background:To compare different ultrasound-based international ovarian tumour analysis (IOTA) strategies and risk of malignancy index (RMI) for ovarian cancer diagnosis using a meta-analysis approach of centre-specific data from IOTA3.Methods:This prospective multicentre diagnostic accuracy study included 2403 patients with 1423 benign and 980 malignant adnexal masses from 2009 until 2012. All patients underwent standardised transvaginal ultrasonography. Test performance of RMI, subjective assessment (SA) of ultrasound findings, two IOTA risk models (LR1 and LR2), and strategies involving combinations of IOTA simple rules (SRs), simple descriptors (SDs) and LR2 with and without SA was estimated using a meta-analysis approach. Reference standard was histology after surgery.Results:The areas under the receiver operator characteristic curves of LR1, LR2, SA and RMI were 0.930 (0.917–0.942), 0.918 (0.905–0.930), 0.914 (0.886–0.936) and 0.875 (0.853–0.894). Diagnostic one-step and two-step strategies using LR1, LR2, SR and SD achieved summary estimates for sensitivity 90–96%, specificity 74–79% and diagnostic odds ratio (DOR) 32.8–50.5. Adding SA when IOTA methods yielded equivocal results improved performance (DOR 57.6–75.7). Risk of Malignancy Index had sensitivity 67%, specificity 91% and DOR 17.5.Conclusions:This study shows all IOTA strategies had excellent diagnostic performance in comparison with RMI. The IOTA strategy chosen may be determined by clinical preference.

A comparison between an ultrasound based prediction model (LR2) and the Risk of Ovarian Malignancy Algorithm (ROMA) to assess the risk of malignancy in women with an adnexal mass

OBJECTIVE The identification of novel biomarkers led to the development of the ROMA algorithm incorporating both HE4 and CA125 to predict malignancy in women with a pelvic mass. An ultrasound based prediction model (LR2) developed by the International Ovarian Tumor Analysis (IOTA) study offers better diagnostic performance than CA125 alone. In this study we compared the diagnostic accuracy between LR2 and ROMA. METHODS This study included women with a pelvic mass scheduled for surgery and enrolled in a previous prospective diagnostic accuracy study. Experienced ultrasound examiners, general gynecologists and trainees supervised by one of the experts performed the preoperative transvaginal ultrasound examinations. Serum biomarkers were taken prior to surgery. Accuracy of LR2 and ROMA was estimated at completion of this study and did not form part of the decision making process. Final outcome was histology of removed tissues and surgical stage if relevant. RESULTS In total 360 women were evaluated. 216 women had benign disease and 144 a malignancy. Overall test performance of LR2 (AUC 0.952) with 94% sensitivity and 82% specificity was significantly better than ROMA (AUC 0.893) with 84% sensitivity and 80% specificity. Difference in AUC was 0.059 (95% CI: 0.026-0.091; P-value 0.0004). Similar results were obtained when stratified for menopausal status. CONCLUSION LR2 shows a better diagnostic performance than ROMA for the characterization of a pelvic mass in both pre- and postmenopausal women. These findings suggest that HE4 and CA125 may not play an important role in the diagnosis of ovarian cancer if good quality ultrasonography is available.

A multicenter prospective external validation of the diagnostic performance of IOTA simple descriptors and rules to characterize ovarian masses

OBJECTIVES To evaluate the diagnostic performance of the IOTA (International Ovarian Tumor Analysis group) (clinically oriented three-step strategy for preoperative characterization of ovarian masses when ultrasonography is performed by examiners with different background training and experience. METHODS A 27-month prospective multicenter cross-sectional study was performed. 36 level II ultrasound examiners contributed in three UK hospitals. Transvaginal ultrasonography was performed using a standardized approach. Step one uses simple descriptors (SD), step two ultrasound simple rules (SR) and step three subjective assessment of ultrasound images (SA) by examiners. The final outcome was findings at surgery and the histological diagnosis of surgically removed masses. RESULTS 1165 women with adnexal masses underwent transvaginal ultrasonography, 301 had surgery. Prevalence of malignancy was 31% (n=92). SD were able to classify 46% of the masses into benign or malignant (step one), with a sensitivity of 93% and specificity of 97%. Applying SD followed by SR to residual unclassified masses by SD enabled 89% of all masses (n=268) to be classified with a sensitivity 95% of and specificity of 95%. SA was then used to evaluate the rest of the masses. Compared to the risk of malignancy index (RMI), the sensitivity and specificity for the three-step (SD+SR+SA) strategy were 93% (95% CI: 86-97%) and 92% (95% CI: 87-95%) vs. 72% (95% CI: 62-80%) and 95% (95% CI: 91-97%) for RMI, respectively. CONCLUSION The IOTA three-step strategy shows good test performance on external validation in the hands of ultrasonography examiners with different background training and experience. This performance is considerably better than the RMI.

Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis

INTRODUCTION Many national guidelines concerning the management of ovarian cancer currently advocate the risk of malignancy index (RMI) to characterise ovarian pathology. However, other methods, such as subjective assessment, International Ovarian Tumour Analysis (IOTA) simple ultrasound-based rules (simple rules) and IOTA logistic regression model 2 (LR2) seem to be superior to the RMI. Our objective was to compare the diagnostic accuracy of subjective assessment, simple rules, LR2 and RMI for differentiating benign from malignant adnexal masses prior to surgery. MATERIALS AND METHODS MEDLINE, EMBASE and CENTRAL were searched (January 1990-August 2015). Eligibility criteria were prospective diagnostic studies designed to preoperatively predict ovarian cancer in women with an adnexal mass. RESULTS We analysed 47 articles, enrolling 19,674 adnexal tumours; 13,953 (70.9%) benign and 5721 (29.1%) malignant. Subjective assessment by experts performed best with a pooled sensitivity of 0.93 (95% confidence interval [CI] 0.92-0.95) and specificity of 0.89 (95% CI 0.86-0.92). Simple rules (classifying inconclusives as malignant) (sensitivity 0.93 [95% CI 0.91-0.95] and specificity 0.80 [95% CI 0.77-0.82]) and LR2 (sensitivity 0.93 [95% CI 0.89-0.95] and specificity 0.84 [95% CI 0.78-0.89]) outperformed RMI (sensitivity 0.75 [95% CI 0.72-0.79], specificity 0.92 [95% CI 0.88-0.94]). A two-step strategy using simple rules, when inconclusive added by subjective assessment, matched test performance of subjective assessment by expert examiners (sensitivity 0.91 [95% CI 0.89-0.93] and specificity 0.91 [95% CI 0.87-0.94]). CONCLUSIONS A two-step strategy of simple rules with subjective assessment for inconclusive tumours yielded best results and matched test performance of expert ultrasound examiners. The LR2 model can be used as an alternative if an expert is not available.

Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study.

Background:The International Ovarian Tumour Analysis (IOTA) group have developed the ADNEX (The Assessment of Different NEoplasias in the adneXa) model to predict the risk that an ovarian mass is benign, borderline, stage I, stages II–IV or metastatic. We aimed to externally validate the ADNEX model in the hands of examiners with varied training and experience.Methods:This was a multicentre cross-sectional cohort study for diagnostic accuracy. Patients were recruited from three cancer centres in Europe. Patients who underwent transvaginal ultrasonography and had a histological diagnosis of surgically removed tissue were included. The diagnostic performance of the ADNEX model with and without the use of CA125 as a predictor was calculated.Results:Data from 610 women were analysed. The overall prevalence of malignancy was 30%. The area under the receiver operator curve (AUC) for the ADNEX diagnostic performance to differentiate between benign and malignant masses was 0.937 (95% CI: 0.915–0.954) when CA125 was included, and 0.925 (95% CI: 0.902–0.943) when CA125 was excluded. The calibration plots suggest good correspondence between the total predicted risk of malignancy and the observed proportion of malignancies. The model showed good discrimination between the different subtypes.Conclusions:The performance of the ADNEX model retains its performance on external validation in the hands of ultrasound examiners with varied training and experience.

The characteristic ultrasound features of specific types of ovarian pathology (Review)

Characterizing ovarian masses enables patients with malignancy to be appropriately triaged for treatment by subspecialist gynecological oncologists, which has been shown to optimize care and improve survival. Furthermore, correctly classifying benign masses facilitates the selection of patients with ovarian pathology that may either not require intervention, or be suitable for minimal access surgery if intervention is required. However, predicting whether a mass is benign or malignant is not the only clinically relevant information that we need to know before deciding on appropriate treatment. Knowing the specific histology of a mass is becoming of increasing importance as management options become more tailored to the individual patient. For example predicting a mucinous borderline tumor gives the opportunity for fertility sparing surgery, and will highlight the need for further gastrointestinal assessment. For benign disease, predicting the presence of an endometrioma and possible deeply infiltrating endometriosis is important when considering both who should perform and the extent of surgery. An examiner’s subjective assessment of the morphological and vascular features of a mass using ultrasonography has been shown to be highly effective for predicting whether a mass is benign or malignant. Many masses also have features that enable a reliable diagnosis of the specific pathology of a particular mass to be made. In this narrative review we aim to describe the typical morphological features seen on ultrasound of different adnexal masses and illustrate these by showing representative ultrasound images.