A. Sayasneh

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.

Predicting successful vaginal birth after Cesarean section using a model based on Cesarean scar features examined by transvaginal sonography: TVS of Cesarean scar to predict successful vaginal birth

To develop a model to predict the success of a trial of vaginal birth after Cesarean section (VBAC) based on sonographic measurements of Cesarean section (CS) scar features, demographic variables and previous obstetric history.

Changes in Cesarean section scar dimensions during pregnancy: a prospective longitudinal study

To describe changes in Cesarean section (CS) scars longitudinally throughout pregnancy, and to relate initial scar measurements, demographic variables and obstetric variables to subsequent changes in scar features and to final pregnancy outcome.

Visibility and measurement of Cesarean section scars in pregnancy: a reproducibility study

To evaluate the visibility of Cesarean section (CS) scars by transvaginal sonography (TVS) in pregnant women, to apply a standardized approach for measuring CS scars and to test its reproducibility throughout the course of pregnancy.

Triaging pregnancies of unknown location: the performance of protocols based on single serum progesterone or repeated serum hCG levels

STUDY QUESTION How does a protocol based on a single serum progesterone measurement perform as a triage tool in women with pregnancy of unknown location (PUL) in comparison to protocols based on serial hCG measurement? SUMMARY ANSWER Triage based on the logistic regression model M4 (using initial hCG and hCG ratio (48 h/0 h)) classifies the majority of PUL into low and high risk groups, in contrast to a progesterone protocol based on a serum level threshold of 10 nmol/l. WHAT IS KNOWN ALREADY Low progesterone has been shown to identify failing pregnancies and those at low risk of complications. A prediction model (M4) based on the initial hCG and the hCG ratio at 0 and 48 h can successfully classify PUL into low and high risk groups. STUDY DESIGN, SIZE AND DURATION A multi-centre diagnostic accuracy study of 1271 women was performed retrospectively on data from women at St. Georges Hospital (SGH, London, UK) between February 2005 and 2006, Queen Charlottes & Chelsea Hospital (QCCH, London, UK) between April 2009 and August 2012, and the Royal Prince Alfred Hospital (RPAH, Sydney, Australia) between February 2008 and October 2011. The end-points were the final observed outcome for each pregnancy as a failed PUL (low risk), intrauterine pregnancy (IUP, low risk), or ectopic pregnancy (EP, high risk), and any interventions or complications for EP during the follow-up period. PARTICIPANTS/MATERIALS, SETTING AND METHODS Complete data were available for initial progesterone, 0/48 h hCG and final outcome in 431 of 534 women (81%) at SGH, 396/585 (68%) at QCCH and 96/152 (63%) at RPAH. Missing values were handled using multiple imputation. Three diagnostic approaches were used to classify PUL as high risk: a range of serum progesterone levels were evaluated (>10, 16 and 20 nmol/l) for the progesterone protocol, risk of EP given by the M4 model ≥5% for the M4-based protocol, and hCG ratio was between 0.87 and 1.66 for hCG cut-offs as previously published. Results were analysed using random intercept models or stratified analysis to account for variability between centres. MAIN RESULTS AND THE ROLE OF CHANCE The progesterone protocol based on levels of >10 nmol/l classified 24% (95% confidence interval 20-28%) of failed PUL, 95% (92-97%) of IUP and 76% (67-83%) of EP as high risk. The M4 protocol classified 14% (11-17%) of failed PUL, 37% (31-43%) of IUP and 84% (76-90%) of EP as high risk. The hCG ratio cut-offs classified 10% (8-12%) of failed PUL, 15% (11-20%) of IUP and 63% (53-71%) of EP as high risk. Using complete cases only, 67% of EP treated with methotrexate (n = 48) and 89% surgically managed (n = 37) were correctly classified by the progesterone protocol, 96 and 81% by M4 protocol and 75 and 65% by hCG ratio cut offs, respectively. LIMITATIONS, REASONS FOR CAUTION Data were incomplete for 103 (19%), 189 (32%) and 56 (37%) patients at SGH, QCCH and RPAH, respectively; however, we are reassured by the minimal differences seen between the results of complete cases and those following imputation of missing values. The variation in the inclusion criteria between the three centres is also a potential limitation of this study; however, it reflects real clinical practice. Furthermore, the hCG ratio cut-offs were not originally developed to optimize triage. WIDER IMPLICATIONS OF THE FINDINGS The results show that serum progesterone is less efficient for triage than serial hCG measurements assessed using the M4 model, the striking difference being serum progesterone places nearly all IUP in the high-risk category. A two-step strategy combining single-visit and two-visit approaches should be investigated. STUDY FUNDING/COMPETING INTERESTS Funding was from Research Foundation-Flanders (FWO). There are no competing interests.

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.

Are serum HE4 or ROMA scores useful to experienced examiners for improving characterization of adnexal masses after transvaginal ultrasonography

To determine whether serum human‐epididymis protein‐4 (HE4) levels or Risk of Ovarian Malignancy Algorithm (ROMA) scores are useful second‐stage tests for tumors thought to be difficult to characterize as benign or malignant on the basis of ultrasound findings by experienced examiners, and to investigate whether adding information on serum HE4 levels or ROMA scores to ultrasound findings improves diagnostic performance.

Do pocket-sized ultrasound machines have the potential to be used as a tool to triage patients in obstetrics and gynecology?

To evaluate the performance and potential impact on patient management of a pocket‐sized ultrasound machine (PUM) in comparison to high‐specification ultrasound machines (HSUM).

External validation of models and simple scoring systems to predict miscarriage in intrauterine pregnancies of uncertain viability

STUDY QUESTION Does a logistic regression model and scoring system to predict viability of an intrauterine pregnancy of uncertain viability (PUV) perform as well in an independent patient group as the original patient group? SUMMARY ANSWER The model and scoring system showed good performance on external validation confirming their value for the prediction of miscarriage/viability in PUV patients up to 11-14 weeks of gestation. WHAT IS KNOWN ALREADY Several individual ultrasound and demographic factors have been described as predictors for miscarriage. A logistic regression model and simple scoring system using basic clinical and ultrasound features, such as maternal age, bleeding score, mean gestational sac diameter (MSD) and presence or absence of yolk sac, have been developed to allow patient-specific prediction of viability of PUV beyond the first trimester. STUDY DESIGN, SIZE, DURATION Prospective observational external validation cohort study in two inner city early pregnancy assessment units over a period of 18 months. PARTICIPANTS/MATERIALS, SETTING, METHODS All consecutive women with a PUV were recruited. Ultrasound (mean sac diameter and presence of yolk sac) and demographic variables (maternal age, bleeding score and gestational age) were noted. The outcome measure was first trimester (11-14 week) viability. Women with unknown first trimester outcome were excluded. Receiver operating characteristic (ROC) curves and calibration plots were constructed. Test performance was compared with the original development data set. A new model and scoring system, which did not include gestational age, was built and evaluated. MAIN RESULTS AND THE ROLE OF CHANCE Of the 575 women who were recruited, first trimester outcome was known for 89.2% (n = 513). The model could only be validated in 400 patients, due to missing values in model variables and outcome. The model predicted viability with an area under the ROC curve (AUC) of 0.845 [95% confidence interval (CI), 0.806-0.884] compared with 0.774 (95% CI, 0.701-0.848) in the original study. The AUC for the scoring system was 0.832 (95% CI, 0.792-0.872) compared with 0.771 (95% CI, 0.698-0.844) from the original study data set. The new model and the scoring system, excluding gestational age, could be evaluated on 503 patients and resulted in an AUC of 0.801 (95% CI, 0.765-0.841) for the model and 0.773 (95% CI, 0.733-0.812) for the scoring system. LIMITATIONS, REASONS FOR CAUTION Approximately 22% of patients could not be validated due to missing variables and for 11% of patients the first trimester outcome was unknown. WIDER IMPLICATIONS OF THE FINDINGS Both the model and the scoring system showed excellent performance on external validation confirming their generalizability and utility in prediction of viability beyond the first trimester in clinical practice. An advantage of the mathematical models original Mo and new Mn and scoring systems original SSo and new SSn is that they can provide women with an individualized probability of the viability of their pregnancy using only demographic information, symptoms and TVS findings. Furthermore, the risk of miscarriage can be given immediately following examination. STUDY FUNDING/COMPETING INTEREST(S) T.B. is supported by the Imperial Healthcare NHS Trust NIHR Biomedical Research Centre. This research is supported by Research Council KUL GOA MaNet, iMinds 2012, Belgian Federal Science Policy Office IUAP P719. VVB is a postdoctoral fellow of the Research Foundation - Flanders (FWO). There are no conflicts of interest.