MetDecode: methylation-based deconvolution of cell-free DNA for noninvasive multi-cancer typing.

MOTIVATION: Circulating-cell free DNA (cfDNA) is widely explored as a noninvasive biomarker for cancer screening and diagnosis. The ability to decode the cells of origin in cfDNA would provide biological insights into pathophysiological mechanisms, aiding in cancer characterization and directing clinical management and follow-up. RESULTS: We developed a DNA methylation signature-based deconvolution algorithm, MetDecode, for cancer tissue origin identification. We built a reference atlas exploiting de novo and published whole-genome methylation sequencing data for colorectal, breast, ovarian, and cervical cancer, and blood-cell-derived entities. MetDecode models the contributors absent in the atlas with methylation patterns learnt on-the-fly from the input cfDNA methylation profiles. In addition, our model accounts for the coverage of each marker region to alleviate potential sources of noise. In-silico experiments showed a limit of detection down to 2.88% of tumor tissue contribution in cfDNA. MetDecode produced Pearson correlation coefficients above 0.95 and outperformed other methods in simulations (P < 0.001; T-test; one-sided). In plasma cfDNA profiles from cancer patients, MetDecode assigned the correct tissue-of-origin in 84.2% of cases. In conclusion, MetDecode can unravel alterations in the cfDNA pool components by accurately estimating the contribution of multiple tissues, while supplied with an imperfect reference atlas. AVAILABILITY AND IMPLEMENTATION: MetDecode is available at https://github.com/JorisVermeeschLab/MetDecode.

Risk-prediction models in postmenopausal patients with symptoms of suspected ovarian cancer in the UK (ROCkeTS): a multicentre, prospective diagnostic accuracy study.

BACKGROUND: Multiple risk-prediction models are used in clinical practice to triage patients as being at low risk or high risk of ovarian cancer. In the ROCkeTS study, we aimed to identify the best diagnostic test for ovarian cancer in symptomatic patients, through head-to-head comparisons of risk-prediction models, in a real-world setting. Here, we report the results for the postmenopausal cohort. METHODS: In this multicentre, prospective diagnostic accuracy study, we recruited newly presenting female patients aged 16-90 years with non-specific symptoms and raised CA125 or abnormal ultrasound results (or both) who had been referred via rapid access, elective clinics, or emergency presentations from 23 hospitals in the UK. Patients with normal CA125 and simple ovarian cysts of smaller than 5 cm in diameter, active non-ovarian malignancy, or previous ovarian malignancy, or those who were pregnant or declined a transvaginal scan, were ineligible. In this analysis, only postmenopausal participants were included. Participants completed a symptom questionnaire, gave a blood sample, and had transabdominal and transvaginal ultrasounds performed by International Ovarian Tumour Analysis consortium (IOTA)-certified sonographers. Index tests were Risk of Malignancy 1 (RMI1) at a threshold of 200, Risk of Malignancy Algorithm (ROMA) at multiple thresholds, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) at thresholds of 3% and 10%, IOTA SRRisk model at thresholds of 3% and 10%, IOTA Simple Rules (malignant vs benign, or inconclusive), and CA125 at 35 IU/mL. In a post-hoc analysis, the Ovarian Adnexal and Reporting Data System (ORADS) at 10% was derived from IOTA ultrasound variables using established methods since ORADS was described after completion of recruitment. Index tests were conducted by study staff masked to the results of the reference standard. The comparator was RMI1 at the 250 threshold (the current UK National Health Service standard of care). The reference standard was surgical or biopsy tissue histology or cytology within 3 months, or a self-reported diagnosis of ovarian cancer at 12 month follow-up. The primary outcome was diagnostic accuracy at predicting primary invasive ovarian cancer versus benign or normal histology, assessed by analysing the sensitivity, specificity, C-index, area under receiver operating characteristic curve, positive and negative predictive values, and calibration plots in participants with conclusive reference standard results and available index test data. This study is registered with the International Standard Randomised Controlled Trial Number registry (ISRCTN17160843). FINDINGS: Between July 13, 2015, and Nov 30, 2018, 1242 postmenopausal patients were recruited, of whom 215 (17%) had primary ovarian cancer. 166 participants had missing, inconclusive, or other reference standard results; therefore, data from a maximum of 1076 participants were used to assess the index tests for the primary outcome. Compared with RMI1 at 250 (sensitivity 82·9% [95% CI 76·7 to 88·0], specificity 87·4% [84·9 to 89·6]), IOTA ADNEX at 10% was more sensitive (difference of -13·9% [-20·2 to -7·6], p<0·0001) but less specific (difference of 28·5% [24·7 to 32·3], p<0·0001). ROMA at 29·9 had similar sensitivity (difference of -3·6% [-9·1 to 1·9], p=0·24) but lower specificity (difference of 5·2% [2·5 to 8·0], p=0·0001). RMI1 at 200 had similar sensitivity (difference of -2·1% [-4·7 to 0·5], p=0·13) but lower specificity (difference of 3·0% [1·7 to 4·3], p<0·0001). IOTA SRRisk model at 10% had similar sensitivity (difference of -4·3% [-11·0 to -2·3], p=0·23) but lower specificity (difference of 16·2% [12·6 to 19·8], p<0·0001). IOTA Simple Rules had similar sensitivity (difference of -1·6% [-9·3 to 6·2], p=0·82) and specificity (difference of -2·2% [-5·1 to 0·6], p=0·14). CA125 at 35 IU/mL had similar sensitivity (difference of -2·1% [-6·6 to 2·3], p=0·42) but higher specificity (difference of 6·7% [4·3 to 9·1], p<0·0001). In a post-hoc analysis, when compared with RMI1 at 250, ORADS achieved similar sensitivity (difference of -2·1%, 95% CI -8·6 to 4·3, p=0·60) and lower specificity (difference of 10·2%, 95% CI 6·8 to 13·6, p<0·0001). INTERPRETATION: In view of its higher sensitivity than RMI1 at 250, despite some loss in specificity, we recommend that IOTA ADNEX at 10% should be considered as the new standard-of-care diagnostic in ovarian cancer for postmenopausal patients. FUNDING: UK National Institute of Heath Research.

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.

Symptom-triggered testing detects early stage and low volume resectable advanced stage ovarian cancer.

OBJECTIVE: Symptom-triggered testing for ovarian cancer was introduced to the UK whereby symptomatic women undergo an ultrasound scan and serum CA125, and are referred to hospital within 2 weeks if these are abnormal. The potential value of symptom-triggered testing in the detection of early-stage disease or low tumor burden remains unclear in women with high grade serous ovarian cancer. In this descriptive study, we report on the International Federation of Gynecology and Obstetrics (FIGO) stage, disease distribution, and complete cytoreduction rates in women presenting via the fast-track pathway and who were diagnosed with high grade serous ovarian cancer. METHODS: We analyzed the dataset from Refining Ovarian Cancer Test accuracy Scores (ROCkeTS), a single-arm prospective diagnostic test accuracy study recruiting from 24 hospitals in the UK. The aim of ROCkeTS is to validate risk prediction models in symptomatic women. We undertook an opportunistic analysis for women recruited between June 2015 to July 2022 and who were diagnosed with high grade serous ovarian cancer via the fast-track pathway. Women presenting with symptoms suspicious for ovarian cancer receive a CA125 blood test and an ultrasound scan if the CA125 level is abnormal. If either of these is abnormal, women are referred to secondary care within 2 weeks. Histology details were available on all women who underwent surgery or biopsy within 3 months of recruitment. Women who did not undergo surgery or biopsy at 3 months were followed up for 12 months as per the national guidelines in the UK. In this descriptive study, we report on patient demographics (age and menopausal status), WHO performance status, FIGO stage at diagnosis, disease distribution (low/pelvic confined, moderate/extending to mid-abdomen, high/extending to upper abdomen) and complete cytoreduction rates in women who underwent surgery. RESULTS: Of 1741 participants recruited via the fast-track pathway, 119 (6.8%) were diagnosed with high grade serous ovarian cancer. The median age was 63 years (range 32-89). Of these, 112 (94.1%) patients had a performance status of 0 and 1, 30 (25.2%) were diagnosed with stages I/II, and the disease distribution was low-to-moderate in 77 (64.7%). Complete and optimal cytoreduction were achieved in 73 (61.3%) and 18 (15.1%). The extent of disease was low in 43 of 119 (36.1%), moderate in 34 of 119 (28.6%), high in 32 of 119 (26.9%), and not available in 10 of 119 (8.4%). Nearly two thirds, that is 78 of 119 (65.5%) women with high grade serous ovarian cancer, underwent primary debulking surgery, 36 of 119 (30.3%) received neoadjuvant chemotherapy followed by interval debulking surgery, and 5 of 119 (4.2%) women did not undergo surgery. CONCLUSION: Our results demonstrate that one in four women identified with high grade serous ovarian cancer through the fast-track pathway following symptom-triggered testing was diagnosed with early-stage disease. Symptom-triggered testing may help identify women with a low disease burden, potentially contributing to high complete cytoreduction rates.

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.