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.

A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study.

Tuning hyperparameters, such as the regularization parameter in Ridge or Lasso regression, is often aimed at improving the predictive performance of risk prediction models. In this study, various hyperparameter tuning procedures for clinical prediction models were systematically compared and evaluated in low-dimensional data. The focus was on out-of-sample predictive performance (discrimination, calibration, and overall prediction error) of risk prediction models developed using Ridge, Lasso, Elastic Net, or Random Forest. The influence of sample size, number of predictors and events fraction on performance of the hyperparameter tuning procedures was studied using extensive simulations. The results indicate important differences between tuning procedures in calibration performance, while generally showing similar discriminative performance. The one-standard-error rule for tuning applied to cross-validation (1SE CV) often resulted in severe miscalibration. Standard non-repeated and repeated cross-validation (both 5-fold and 10-fold) performed similarly well and outperformed the other tuning procedures. Bootstrap showed a slight tendency to more severe miscalibration than standard cross-validation-based tuning procedures. Differences between tuning procedures were larger for smaller sample sizes, lower events fractions and fewer predictors. These results imply that the choice of tuning procedure can have a profound influence on the predictive performance of prediction models. The results support the application of standard 5-fold or 10-fold cross-validation that minimizes out-of-sample prediction error. Despite an increased computational burden, we found no clear benefit of repeated over non-repeated cross-validation for hyperparameter tuning. We warn against the potentially detrimental effects on model calibration of the popular 1SE CV rule for tuning prediction models in low-dimensional settings.

Investigating harms of testing for ovarian cancer - psychological outcomes and cancer conversion rates in women with symptoms of ovarian cancer: A cohort study embedded in the multicentre ROCkeTS prospective diagnostic study.

OBJECTIVE: To investigate psychological correlates in women referred with suspected ovarian cancer via the fast-track pathway, explore how anxiety and distress levels change at 12 months post-testing, and report cancer conversion rates by age and referral pathway. DESIGN: Single-arm prospective cohort study. SETTING: Multicentre. Secondary care including outpatient clinics and emergency admissions. POPULATION: A cohort of 2596 newly presenting symptomatic women with a raised CA125 level, abnormal imaging or both. METHODS: Women completed anxiety and distress questionnaires at recruitment and at 12 months for those who had not undergone surgery or a biopsy within 3 months of recruitment. MAIN OUTCOME MEASURES: Anxiety and distress levels measured using a six-item short form of the State-Trait Anxiety Inventory (STAI-6) and the Impact of Event Scale - Revised (IES-r) questionnaire. Ovarian cancer (OC) conversion rates by age, menopausal status and referral pathway. RESULTS: Overall, 1355/2596 (52.1%) and 1781/2596 (68.6%) experienced moderate-to-severe distress and anxiety, respectively, at recruitment. Younger age and emergency presentations had higher distress levels. The clinical category for anxiety and distress remained unchanged/worsened in 76% of respondents at 12 months, despite a non-cancer diagnosis. The OC rates by age were 1.6% (95% CI 0.5%-5.9%) for age <40 years and 10.9% (95% CI 8.7%-13.6%) for age ≥40 years. In women referred through fast-track pathways, 3.3% (95% CI 1.9%-5.7%) of pre- and 18.5% (95% CI 16.1%-21.0%) of postmenopausal women were diagnosed with OC. CONCLUSIONS: Women undergoing diagnostic testing display severe anxiety and distress. Younger women are especially vulnerable and should be targeted for support. Women under the age of 40 years have low conversion rates and we advocate reducing testing in this group to reduce the harms of testing.

O-RADS US v2022: An Update from the American College of Radiology's Ovarian-Adnexal Reporting and Data System US Committee.

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

Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.

BACKGROUND: Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage I primary invasive, stage II-IV primary invasive, or secondary metastatic. METHODS: This retrospective cohort study used 5909 patients recruited from 1999 to 2012 for model development, and 3199 patients recruited from 2012 to 2015 for model validation. Patients were recruited at oncology referral or general centers and underwent an ultrasound examination and surgery ≤ 120 days later. We developed models using standard multinomial logistic regression (MLR), Ridge MLR, random forest (RF), XGBoost, neural networks (NN), and support vector machines (SVM). We used nine clinical and ultrasound predictors but developed models with or without CA125. RESULTS: Most tumors were benign (3980 in development and 1688 in validation data), secondary metastatic tumors were least common (246 and 172). The c-statistic (AUROC) to discriminate benign from any type of malignant tumor ranged from 0.89 to 0.92 for models with CA125, from 0.89 to 0.91 for models without. The multiclass c-statistic ranged from 0.41 (SVM) to 0.55 (XGBoost) for models with CA125, and from 0.42 (SVM) to 0.51 (standard MLR) for models without. Multiclass calibration was best for RF and XGBoost. Estimated probabilities for a benign tumor in the same patient often differed by more than 0.2 (20% points) depending on the model. Net Benefit for diagnosing malignancy was similar for algorithms at the commonly used 10% risk threshold, but was slightly higher for RF at higher thresholds. Comparing models, between 3% (XGBoost vs. NN, with CA125) and 30% (NN vs. SVM, without CA125) of patients fell on opposite sides of the 10% threshold. CONCLUSION: Although several models had similarly good performance, individual probability estimates varied substantially.

Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets.

BACKGROUND: Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. METHODS: By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. RESULTS: Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. CONCLUSIONS: This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

Similar long-term recurrence rates with cystectomy and CO2 laser vaporization for endometrioma: a retrospective study.

RESEARCH QUESTION: Is there a difference in recurrence rate of endometrioma(s) after cystectomy versus CO2 laser vaporization of the cyst wall? DESIGN: This single-centre retrospective study included 270 patients undergoing laparoscopic surgery for endometriomas between January 2010 and December 2014, stratified according to the surgical technique used. All 270 included patients underwent complete laparoscopic surgery for endometrioma(s): 155 underwent cystectomy, 63 complete CO2 laser vaporization of the cyst wall and 52 a mixed technique. The primary outcome studied was the difference in recurrence rate between the cystectomy group and the CO2 laser vaporization group. RESULTS: The mean duration of follow-up was 58 (±34) months. Imaging-based recurrence (any cyst size) was reported in 9.9% of patients (n = 12/121) treated with cystectomy and in 13.3% of patients (n = 6/45) who underwent a vaporization (P = 0.577). The need for reintervention for endometrioma(s) was also similar in both groups, with a rate of 3.2% (n = 5/155) after cystectomy and 4.8% (n = 3/63) after vaporization (P = 0.476). Of 160 women who wanted to conceive immediately after surgery, 73.8% became pregnant (72.6% [77/106] in the cystectomy group and 75.9% [41/54] in the vaporization group [P = 0.310]). Conception occurred mostly by assisted reproductive technology (57.1% [44/77] in the cystectomy group and 70.7% [29/41] in the vaporization group [P = 0.074]). CONCLUSIONS: Similar rates of recurrence for endometrioma(s) were observed after cystectomy versus CO2 laser vaporization. As other studies have suggested that CO2 laser vaporization may be less harmful to the normal ovarian tissue, it can be considered as a safe alternative for cystectomy in women wishing to preserve their reproductive potential.

Evaluating cut-off levels for progesterone, ß human chorionic gonadotropin and ß human chorionic gonadotropin ratio to exclude pregnancy viability in women with a pregnancy of unknown location: A prospective multicenter cohort study.

INTRODUCTION: There is no global agreement on how to best determine pregnancy of unknown location viability and location using biomarkers. Measurements of progesterone and ß human chorionic gonadotropin (ßhCG) are still used in clinical practice to exclude the possibility of a viable intrauterine pregnancy (VIUP). We evaluate the predictive value of progesterone, ßhCG, and ßhCG ratio cut-off levels to exclude a VIUP in women with a pregnancy of unknown location. MATERIAL AND METHODS: This was a secondary analysis of prospective multicenter study data of consecutive women with a pregnancy of unknown location between January 2015 and 2017 collected from dedicated early pregnancy assessment units of eight hospitals. Single progesterone and serial ßhCG measurements were taken. Women were followed up until final pregnancy outcome between 11 and 14 weeks of gestation was confirmed using transvaginal ultrasonography: (1) VIUP, (2) non-viable intrauterine pregnancy or failed pregnancy of unknown location, and (3) ectopic pregnancy or persisting pregnancy of unknown location. The predictive value of cut-off levels for ruling out VIUP were evaluated across a range of values likely to be encountered clinically for progesterone, ßhCG, and ßhCG ratio. RESULTS: Data from 2507 of 3272 (76.6%) women were suitable for analysis. All had data for ßhCG levels, 2248 (89.7%) had progesterone levels, and 1809 (72.2%) had ßhCG ratio. The likelihood of viability falls with the progesterone level. Although the median progesterone level associated with viability was 59 nmol/L, VIUP were identified with levels as low as 5 nmol/L. No single ßhCG cut-off reliably ruled out the presence of viability with certainty, even when the level was more than 3000 IU/L, there were 39/358 (11%) women who had a VIUP. The probability of viability decreases with the ßhCG ratio. Although the median ßhCG ratio associated with viability was 2.26, VIUP were identified with ratios as low as 1.02. A progesterone level below 2 nmol/L and ßhCG ratio below 0.87 were unlikely to be associated with viability but were not definitive when considering multiple imputation. CONCLUSIONS: Cut-off levels for ßhCG, ßhCG ratio, and progesterone are not safe to be used clinically to exclude viability in early pregnancy. Although ßhCG ratio and progesterone have slightly better performance in comparison, single ßhCG used in this manner is highly unreliable.

The Risk of Endometrial Malignancy and Other Endometrial Pathology in Women with Abnormal Uterine Bleeding: An Ultrasound-Based Model Development Study by the IETA Group.

OBJECTIVES: The aim of this study was to develop a model that can discriminate between different etiologies of abnormal uterine bleeding. DESIGN: The International Endometrial Tumor Analysis 1 study is a multicenter observational diagnostic study in 18 bleeding clinics in 9 countries. Consecutive women with abnormal vaginal bleeding presenting for ultrasound examination (n = 2,417) were recruited. The histology was obtained from endometrial sampling, D&C, hysteroscopic resection, hysterectomy, or ultrasound follow-up for >1 year. METHODS: A model was developed using multinomial regression based on age, body mass index, and ultrasound predictors to distinguish between: (1) endometrial atrophy, (2) endometrial polyp or intracavitary myoma, (3) endometrial malignancy or atypical hyperplasia, (4) proliferative/secretory changes, endometritis, or hyperplasia without atypia and validated using leave-center-out cross-validation and bootstrapping. The main outcomes are the model's ability to discriminate between the four outcomes and the calibration of risk estimates. RESULTS: The median age in 2,417 women was 50 (interquartile range 43-57). 414 (17%) women had endometrial atrophy; 996 (41%) had a polyp or myoma; 155 (6%) had an endometrial malignancy or atypical hyperplasia; and 852 (35%) had proliferative/secretory changes, endometritis, or hyperplasia without atypia. The model distinguished well between malignant and benign histology (c-statistic 0.88 95% CI: 0.85-0.91) and between all benign histologies. The probabilities for each of the four outcomes were over- or underestimated depending on the centers. LIMITATIONS: Not all patients had a diagnosis based on histology. The model over- or underestimated the risk for certain outcomes in some centers, indicating local recalibration is advisable. CONCLUSIONS: The proposed model reliably distinguishes between four histological outcomes. This is the first model to discriminate between several outcomes and is the only model applicable when menopausal status is uncertain. The model could be useful for patient management and counseling, and aid in the interpretation of ultrasound findings. Future research is needed to externally validate and locally recalibrate the model.

Evaluation of Adnexal Masses: The IOTA Concept.

Ovarian lesions have a wide range of sonomorphological features with numerous different underlying benign and malignant histologies. Based on the studies conducted by the International Ovarian Tumor Analysis (IOTA) group, ovarian masses can currently be reliably characterized by ultrasound. In the following article, we explain how to use the IOTA terms and definitions and we provide insight into how to safely triage patients with an ovarian mass.

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.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

Development of organoids from mouse and human endometrium showing endometrial epithelium physiology and long-term expandability.

The endometrium, which is of crucial importance for reproduction, undergoes dynamic cyclic tissue remodeling. Knowledge of its molecular and cellular regulation is poor, primarily owing to a lack of study models. Here, we have established a novel and promising organoid model from both mouse and human endometrium. Dissociated endometrial tissue, embedded in Matrigel under WNT-activating conditions, swiftly formed organoid structures that showed long-term expansion capacity, and reproduced the molecular and histological phenotype of the tissue's epithelium. The supplemented WNT level determined the type of mouse endometrial organoids obtained: high WNT yielded cystic organoids displaying a more differentiated phenotype than the dense organoids obtained in low WNT. The organoids phenocopied physiological responses of endometrial epithelium to hormones, including increased cell proliferation under estrogen and maturation upon progesterone. Moreover, the human endometrial organoids replicated the menstrual cycle under hormonal treatment at both the morpho-histological and molecular levels. Together, we established an organoid culture system for endometrium, reproducing tissue epithelium physiology and allowing long-term expansion. This novel model provides a powerful tool for studying mechanisms underlying the biology as well as the pathology of this key reproductive organ.

O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee.

The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system is designed to provide consistent interpretations, to decrease or eliminate ambiguity in US reports resulting in a higher probability of accuracy in assigning risk of malignancy to ovarian and other adnexal masses, and to provide a management recommendation for each risk category. It was developed by an international multidisciplinary committee sponsored by the American College of Radiology and applies the standardized reporting tool for US based on the 2018 published lexicon of the O-RADS US working group. For risk stratification, the O-RADS US system recommends six categories (O-RADS 0-5), incorporating the range of normal to high risk of malignancy. This unique system represents a collaboration between the pattern-based approach commonly used in North America and the widely used, European-based, algorithmic-style International Ovarian Tumor Analysis (IOTA) Assessment of Different Neoplasias in the Adnexa model system, a risk prediction model that has undergone successful prospective and external validation. The pattern approach relies on a subgroup of the most predictive descriptors in the lexicon based on a retrospective review of evidence prospectively obtained in the IOTA phase 1-3 prospective studies and other supporting studies that assist in differentiating management schemes in a variety of almost certainly benign lesions. With O-RADS US working group consensus, guidelines for management in the different risk categories are proposed. Both systems have been stratified to reach the same risk categories and management strategies regardless of which is initially used. At this time, O-RADS US is the only lexicon and classification system that encompasses all risk categories with their associated management schemes.

Posttraumatic stress, anxiety and depression following miscarriage and ectopic pregnancy: a multicenter, prospective, cohort study.

BACKGROUND: Early pregnancy losses are common, but their psychologic sequelae are often overlooked. Previous studies have established links between miscarriage and early symptoms of anxiety and depression. However, the incidence of posttraumatic stress symptoms and the psychologic response specifically to ectopic pregnancies have not been investigated. OBJECTIVE: The purpose of this study was to investigate levels of posttraumatic stress, depression, and anxiety in women in the 9 months after early pregnancy loss, with a focus on miscarriage and ectopic pregnancy. Morbidity at 1 month was compared with a control group in healthy pregnancy. STUDY DESIGN: This was a prospective cohort study. Consecutive women were recruited from the early pregnancy and antenatal clinics at 3 London hospitals and received emailed surveys that contained standardized psychologic assessments that included the Hospital Anxiety and Depression Scale and Posttraumatic stress Diagnostic Scale, at 1, 3, and 9 months after loss. Control subjects were assessed after a dating scan. We assessed the proportion of participants who met the screening criteria for posttraumatic stress and moderate/severe anxiety or depression. We used logistic regression to calculate adjusted odds ratios. RESULTS: Seven hundred thirty-seven of 1098 women (67%) with early pregnancy loss (including 537 miscarriages and 116 ectopic pregnancies) and 171 of 187 control subjects (91%) agreed to participate. Four hundred ninety-two of the women with losses (67%) completed the Hospital Anxiety and Depression Scale after 1 month; 426 women (58%) completed it after 3 months, and 338 women (46%) completed it after 9 months. Eighty-seven control subjects (51%) participated. Criteria for posttraumatic stress were met in 29% of women with early pregnancy loss after 1 month and in 18% after 9 months (odds ratio per month, 0.80; 95% confidence interval, 0.72-0.89). Moderate/severe anxiety was reported in 24% after 1 month and in 17% after 9 months (odds ratio per month, 0.69; 95% confidence interval, 0.50-0.94). Moderate/severe depression was reported in 11% of the women after 1 month and 6% of the women after 9 months (odds ratio per month, 0.87; 95% confidence interval, 0.53-1.44). After miscarriage, proportions after 9 months were 16% for posttraumatic stress, 17% for anxiety, and 5% for depression. Corresponding figures after ectopic pregnancy were 21%, 23%, and 11%, respectively. In contrast, among control women with viable pregnancies, 13% reported moderate-to-severe anxiety (odds ratio loss at 1 month vs controls: 2.14; 95% confidence interval, 1.14-4.36), and 2% reported moderate-to-severe depression (odds ratio loss at 1 month vs control subjects: 3.88; 95% confidence interval, 1.27-19.2). CONCLUSION: Women experience high levels of posttraumatic stress, anxiety, and depression after early pregnancy loss. Distress declines over time but remains at clinically important levels at 9 months.

Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.

Although multicenter data are common, many prediction model studies ignore this during model development. The objective of this study is to evaluate the predictive performance of regression methods for developing clinical risk prediction models using multicenter data, and provide guidelines for practice. We compared the predictive performance of standard logistic regression, generalized estimating equations, random intercept logistic regression, and fixed effects logistic regression. First, we presented a case study on the diagnosis of ovarian cancer. Subsequently, a simulation study investigated the performance of the different models as a function of the amount of clustering, development sample size, distribution of center-specific intercepts, the presence of a center-predictor interaction, and the presence of a dependency between center effects and predictors. The results showed that when sample sizes were sufficiently large, conditional models yielded calibrated predictions, whereas marginal models yielded miscalibrated predictions. Small sample sizes led to overfitting and unreliable predictions. This miscalibration was worse with more heavily clustered data. Calibration of random intercept logistic regression was better than that of standard logistic regression even when center-specific intercepts were not normally distributed, a center-predictor interaction was present, center effects and predictors were dependent, or when the model was applied in a new center. Therefore, to make reliable predictions in a specific center, we recommend random intercept logistic regression.

Intrauterine Fluid Instillation and Transtubal Flow: A Randomized Controlled In vitro Trial Comparing Gel and Water.

BACKGROUND: Possible transtubal spillage of malignant cells is a major concern in fluid instillation sonography, as it is in hysteroscopy. This study aims to compare the transtubal flow of gel and saline and validate the clinical hypothesis that application of fluids with higher viscosity causes less spillage. METHODS: Randomized controlled in vitro trial comparing gel and saline infusion on 15 tissue specimens after hysterectomy with bilateral salpingectomy. Instillations are performed with saline and gel dyed with a 1% ink solution. Qualitative assessment of tubal spill is investigated as primary outcome. Secondary outcomes are instillation-volume and -pressure, assessed by measuring endometrial cavity dilation at in vitro ultrasound examination and subjective numeric 10-point scoring of the instillation pressure by a dedicated examiner. RESULTS: Tubal flow was more often observed during saline instillation (odds ratio 4.88, P = 0.008). Median subjectively assessed instillation pressures were nine arbitrary units for gel and three for saline (P < 0.001). Tubal flow occurred from 2 cc onward in the saline group versus five cc in the gel instillation group. Cavitary dilation did not differ between both groups. CONCLUSION: Gel instillation sonography is in vitro associated with less tubal flow and therefore could be a safer diagnostic test compared to saline infusion sonography or hysteroscopy. In vivo studies are necessary to confirm these results.

Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study.

BACKGROUND: Ovarian tumours are usually surgically removed because of the presumed risk of complications. Few large prospective studies on long-term follow-up of adnexal masses exist. We aimed to estimate the cumulative incidence of cyst complications and malignancy during the first 2 years of follow-up after adnexal masses have been classified as benign by use of ultrasonography. METHODS: In the international, prospective, cohort International Ovarian Tumor Analysis Phase 5 (IOTA5) study, patients aged 18 years or older with at least one adnexal mass who had been selected for surgery or conservative management after ultrasound assessment were recruited consecutively from 36 cancer and non-cancer centres in 14 countries. Follow-up of patients managed conservatively is ongoing at present. In this 2-year interim analysis, we analysed patients who were selected for conservative management of an adnexal mass judged to be benign on ultrasound on the basis of subjective assessment of ultrasound images. Conservative management included ultrasound and clinical follow-up at intervals of 3 months and 6 months, and then every 12 months thereafter. The main outcomes of this 2-year interim analysis were cumulative incidence of spontaneous resolution of the mass, torsion or cyst rupture, or borderline or invasive malignancy confirmed surgically in patients with a newly diagnosed adnexal mass. IOTA5 is registered with ClinicalTrials.gov, number NCT01698632, and the central Ethics Committee and the Belgian Federal Agency for Medicines and Health Products, number S51375/B32220095331, and is ongoing. FINDINGS: Between Jan 1, 2012, and March 1, 2015, 8519 patients were recruited to IOTA5. 3144 (37%) patients selected for conservative management were eligible for inclusion in our analysis, of whom 221 (7%) had no follow-up data and 336 (11%) were operated on before a planned follow-up scan was done. Of 2587 (82%) patients with follow-up data, 668 (26%) had a mass that was already in follow-up at recruitment, and 1919 (74%) presented with a new mass at recruitment (ie, not already in follow-up in the centre before recruitment). Median follow-up of patients with new masses was 27 months (IQR 14-38). The cumulative incidence of spontaneous resolution within 2 years of follow-up among those with a new mass at recruitment (n=1919) was 20·2% (95% CI 18·4-22·1), and of finding invasive malignancy at surgery was 0·4% (95% CI 0·1-0·6), 0·3% (<0·1-0·5) for a borderline tumour, 0·4% (0·1-0·7) for torsion, and 0·2% (<0·1-0·4) for cyst rupture. INTERPRETATION: Our results suggest that the risk of malignancy and acute complications is low if adnexal masses with benign ultrasound morphology are managed conservatively, which could be of value when counselling patients, and supports conservative management of adnexal masses classified as benign by use of ultrasound. FUNDING: Research Foundation Flanders, KU Leuven, Swedish Research Council.

Three myths about risk thresholds for prediction models.

BACKGROUND: Clinical prediction models are useful in estimating a patient's risk of having a certain disease or experiencing an event in the future based on their current characteristics. Defining an appropriate risk threshold to recommend intervention is a key challenge in bringing a risk prediction model to clinical application; such risk thresholds are often defined in an ad hoc way. This is problematic because tacitly assumed costs of false positive and false negative classifications may not be clinically sensible. For example, when choosing the risk threshold that maximizes the proportion of patients correctly classified, false positives and false negatives are assumed equally costly. Furthermore, small to moderate sample sizes may lead to unstable optimal thresholds, which requires a particularly cautious interpretation of results. MAIN TEXT: We discuss how three common myths about risk thresholds often lead to inappropriate risk stratification of patients. First, we point out the contexts of counseling and shared decision-making in which a continuous risk estimate is more useful than risk stratification. Second, we argue that threshold selection should reflect the consequences of the decisions made following risk stratification. Third, we emphasize that there is usually no universally optimal threshold but rather that a plausible risk threshold depends on the clinical context. Consequently, we recommend to present results for multiple risk thresholds when developing or validating a prediction model. CONCLUSION: Bearing in mind these three considerations can avoid inappropriate allocation (and non-allocation) of interventions. Using discriminating and well-calibrated models will generate better clinical outcomes if context-dependent thresholds are used.

Sample size considerations and predictive performance of multinomial logistic prediction models.

Multinomial Logistic Regression (MLR) has been advocated for developing clinical prediction models that distinguish between three or more unordered outcomes. We present a full-factorial simulation study to examine the predictive performance of MLR models in relation to the relative size of outcome categories, number of predictors and the number of events per variable. It is shown that MLR estimated by Maximum Likelihood yields overfitted prediction models in small to medium sized data. In most cases, the calibration and overall predictive performance of the multinomial prediction model is improved by using penalized MLR. Our simulation study also highlights the importance of events per variable in the multinomial context as well as the total sample size. As expected, our study demonstrates the need for optimism correction of the predictive performance measures when developing the multinomial logistic prediction model. We recommend the use of penalized MLR when prediction models are developed in small data sets or in medium sized data sets with a small total sample size (ie, when the sizes of the outcome categories are balanced). Finally, we present a case study in which we illustrate the development and validation of penalized and unpenalized multinomial prediction models for predicting malignancy of ovarian cancer.