Dirk Timmerman

Diagnostic tests for ovarian cancer in premenopausal women with non-specific symptoms (ROCkeTS): prospective, multicentre, cohort study

Abstract Objective To investigate the accuracy of risk prediction models and scores for diagnosing ovarian cancer in premenopausal women presenting to secondary care with symptoms and abnormal test results. Design Prospective cohort study. Setting Secondary care in 23 hospitals in the UK between June 2015 and March 2023. Participants Premenopausal women presenting with non-specific symptoms, and raised serum levels of cancer antigen 125 or abnormal imaging results, were prospectively recruited, predominantly referred through the NHS urgent suspected cancer pathway from primary care. A head-to-head comparison of the accuracy of the six risk prediction models and scores was conducted using donated blood and ultrasound scans performed by NHS staff trained in the use of International Ovarian Tumour Analysis (IOTA) imaging terminology. The index tests used were Risk of Malignancy Index 1 (with pre-stated thresholds of 200, 250), Risk of Malignancy Algorithm (7.4%, 11.4%, 12.5%, 13.1%), IOTA Assessment of Different Neoplasias in the adnEXa (ADNEX) (3%, 10%), IOTA simple rules risk model (3%, 10%), IOTA simple rules, and cancer antigen 125 (CA 125, 87 IU/mL). Participants were classified as having primary invasive ovarian cancer versus having benign or normal pathology according to the reference standard determined from surgical specimens or biopsies by histology or cytology, if undertaken, or else at 12 month follow-up. After June 2018, because of covid restrictions and concerns about sample size, recruitment was restricted to only women undergoing surgery within three months of presentation to clinic (in whom ovarian cancer was more likely). Main outcome measures 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. Results 88 of 1211 premenopausal women received diagnoses of primary ovarian cancer: 49 of 857 women in the pre-June 2018 cohort (prevalence of 5.7%) and 39 of 354 women in the post-June 2018 cohort (11.0%). For the diagnosis of primary ovarian cancer (n=799 women, after exclusion of 58 other diagnoses), Risk of Malignancy Index 1 at the 250 threshold had a sensitivity of 42.6% (95% confidence interval (CI) 28.3 to 57.8; specificity 96.5%, 94.7 to 97.8). Compared with Risk of Malignancy Index 1 at the 250 threshold, CA 125 and all other tests had higher sensitivity (CA 125 at 87 IU/mL threshold: 55.1%, 40.2 to 69.3, P=0.06; Risk of Malignancy Algorithm at 11.4% threshold: 79.2%, 65.0 to 89.5, P<0.001; IOTA ADNEX at 10% threshold: 89.1%, 76.4 to 96.4, P<0.001; IOTA simple rules risk at 10% threshold: 83.0%, 69.2 to 92.4, P<0.001; IOTA simple rules: 75.0%, 56.6 to 88.5, P=0.01) and lower specificity (CA 125 at 87 IU/mL threshold: 89.0%, 86.5 to 91.2, P<0.001; Risk of Malignancy Algorithm at 11.4% threshold: 73.1%, 69.6 to 76.3, P<0.001; IOTA ADNEX at 10% threshold: 75.1%, 71.4 to 78.6, P<0.001; IOTA simple rules risk at 10% threshold: 76.0%, 72.4 to 79.3, P<0.001; IOTA simple rules: 95.2%, 93.0 to 96.9, P=0.06). Results for IOTA simple rules were inconclusive in 120 of 799 participants. Analysis of the complete cohort (n=1211), including the 354 premenopausal women with a higher likelihood of developing ovarian cancer, yielded similar results. Conclusions Compared to Risk of Malignancy Index 1 at 250 threshold—the test currently used in NHS secondary care to triage women to tertiary care—most tests improve sensitivity but reduce specificity. Ultrasound triage with the IOTA ADNEX model at 10% in secondary care demonstrated the highest sensitivity gain, with a comparable decline in specificity to other comparator tests. Ultrasound with the IOTA ADNEX model at 10% should be considered the new standard of care test for triaging premenopausal women in secondary care. Implementation should incorporate staff training and quality assurance. Trial registration ISRCTN17160843 .

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

A Microfluidics Approach for Ovarian Cancer Immune Monitoring in an Outpatient Setting

Among cancer diagnoses in women, ovarian cancer has the fifth-highest mortality rate. Current treatments are unsatisfactory, and new therapies are highly needed. Immunotherapies show great promise but have not reached their full potential in ovarian cancer patients. Implementation of an immune readout could offer better guidance and development of immunotherapies. However, immune profiling is often performed using a flow cytometer, which is bulky, complex, and expensive. This equipment is centralized and operated by highly trained personnel, making it cumbersome and time-consuming. We aim to develop a disposable microfluidic chip capable of performing an immune readout with the sensitivity needed to guide diagnostic decision making as close as possible to the patient. As a proof of concept of the fluidics module of this concept, acquisition of a limited immune panel based on CD45, CD8, programmed cell death protein 1 (PD1), and a live/dead marker was compared to a conventional flow cytometer (BD FACSymphony). Based on a dataset of peripheral blood mononuclear cells of 15 patients with ovarian cancer across different stages of treatment, we obtained a 99% correlation coefficient for the detection of CD8+PD1+ T cells relative to the total amount of CD45+ white blood cells. Upon further system development comprising further miniaturization of optics, this microfluidics chip could enable immune monitoring in an outpatient setting, facilitating rapid acquisition of data without the need for highly trained staff.

Added value of cell‐free DNA over clinical and ultrasound information for diagnosing ovarian cancer

ABSTRACT Objective We previously proposed two cell‐free (cf) DNA‐based scores (genome‐wide Z ‐score and nucleosome score) as candidate non‐invasive biomarkers to further improve the presurgical diagnosis of ovarian malignancy. We aimed to investigate the added value of these cfDNA‐based scores in combination with the clinical and ultrasound predictors of the Assessment of Different NEoplasias in the adneXa (ADNEX) model to estimate the risk of ovarian malignancy. Methods In this prospective cohort study, 526 patients with an adnexal mass scheduled for surgery were recruited consecutively in three oncology referral centers. All patients underwent a transvaginal ultrasound examination, and adnexal masses were described according to the International Ovarian Tumor Analysis terms and definitions. cfDNA was extracted from preoperative plasma samples and genome‐wide Z ‐scores and nucleosome scores were calculated. Logistic regression models were fitted for ADNEX predictors alone and after inclusion of the cfDNA‐based scores. We report likelihood ratios, area under the receiver‐operating‐characteristics curve (AUC), sensitivity, specificity and net benefit for thresholds between 5% and 40%, to assess the diagnostic performance of the models in discriminating between benign and malignant ovarian masses. Results The study included 272 benign, 86 borderline, 36 Stage‐I invasive, 113 Stage‐II–IV invasive, and 19 secondary metastatic tumors. The likelihood ratios for adding the cfDNA‐based scores to the ADNEX model were statistically significant ( P  < 0.001 for ADNEX without CA 125; P  = 0.001 for ADNEX including CA 125). The accompanying increases in AUC were 0.013 when the cfDNA biomarkers were added to the ADNEX model without CA 125, and 0.003 when added to the ADNEX model including CA 125. Net benefit, sensitivity and specificity were similar for all models. The increase in net benefit at the recommended 10% threshold estimated risk of malignancy when adding the cfDNA‐based scores was 0.0017 and 0.0020, respectively, for the ADNEX model without CA 125 and the ADNEX model with CA 125. According to these results, adding cfDNA markers would require at least 453 patients per additional true‐positive test result at the 10% risk threshold. Conclusion Although statistically significant, cfDNA‐based biomarker scores have limited clinical utility in addition to established clinical and ultrasound‐based ADNEX predictors for discriminating between benign and malignant ovarian masses. © 2025 International Society of Ultrasound in Obstetrics and Gynecology.

Prospective geographical and temporal validation of three published mathematical models to calculate risk of endometrial malignancy in patients with postmenopausal bleeding

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

AbstractAlthough 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.

Response to: Correspondence on "ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors" by Thomassin-Nagarra et al

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.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors

ABSTRACTThe European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence‐based statements on the preoperative diagnosis of ovarian tumors, including imaging techniques, biomarkers and prediction models.ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence‐based, the current literature was reviewed and critically appraised.Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements.This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

Estimating risk of endometrial malignancy and other intracavitary uterine pathology in women without abnormal uterine bleeding using IETA‐1 multinomial regression model: validation study

ABSTRACTObjectivesTo assess the ability of the International Endometrial Tumor Analysis (IETA)‐1 polynomial regression model to estimate the risk of endometrial cancer (EC) and other intracavitary uterine pathology in women without abnormal uterine bleeding.MethodsThis was a retrospective study, in which we validated the IETA‐1 model on the IETA‐3 study cohort (n = 1745). The IETA‐3 study is a prospective observational multicenter study. It includes women without vaginal bleeding who underwent a standardized transvaginal ultrasound examination in one of seven ultrasound centers between January 2011 and December 2018. The ultrasonography was performed either as part of a routine gynecological examination, during follow‐up of non‐endometrial pathology, in the work‐up before fertility treatment or before treatment for uterine prolapse or ovarian pathology. Ultrasonographic findings were described using IETA terminology and were compared with histology, or with results of clinical and ultrasound follow‐up of at least 1 year if endometrial sampling was not performed. The IETA‐1 model, which was created using data from patients with abnormal uterine bleeding, predicts four histological outcomes: (1) EC or endometrial intraepithelial neoplasia (EIN); (2) endometrial polyp or intracavitary myoma; (3) proliferative or secretory endometrium, endometritis, or endometrial hyperplasia without atypia; and (4) endometrial atrophy. The predictors in the model are age, body mass index and seven ultrasound variables (visibility of the endometrium, endometrial thickness, color score, cysts in the endometrium, non‐uniform echogenicity of the endometrium, presence of a bright edge, presence of a single dominant vessel). We analyzed the discriminative ability of the model (area under the receiver‐operating‐characteristics curve (AUC); polytomous discrimination index (PDI)) and evaluated calibration of its risk estimates (observed/expected ratio).ResultsThe median age of the women in the IETA‐3 cohort was 51 (range, 20–85) years and 51% (887/1745) of the women were postmenopausal. Histology showed EC or EIN in 29 (2%) women, endometrial polyps or intracavitary myomas in 1094 (63%), proliferative or secretory endometrium, endometritis, or hyperplasia without atypia in 144 (8%) and endometrial atrophy in 265 (15%) women. The endometrial sample had insufficient material in five (0.3%) cases. In 208 (12%) women who did not undergo endometrial sampling but were followed up for at least 1 year without clinical or ultrasound signs of endometrial malignancy, the outcome was classified as benign. The IETA‐1 model had an AUC of 0.81 (95% CI, 0.73–0.89, n = 1745) for discrimination between malignant (EC or EIN) and benign endometrium, and the observed/expected ratio for EC or EIN was 0.51 (95% CI, 0.32–0.82). The model was able to categorize the four histological outcomes with considerable accuracy: the PDI of the model was 0.68 (95% CI, 0.62–0.73) (n = 1532). The IETA‐1 model discriminated very well between endometrial atrophy and all other intracavitary uterine conditions, with an AUC of 0.96 (95% CI, 0.95–0.98). Including only patients in whom the endometrium was measurable (n = 1689), the model's AUC was 0.83 (95% CI, 0.75–0.91), compared with 0.62 (95% CI, 0.52–0.73) when using endometrial thickness alone to predict malignancy (difference in AUC, 0.21; 95% CI, 0.08–0.32). In postmenopausal women with measurable endometrial thickness (n = 848), the IETA‐1 model gave an AUC of 0.81 (95% CI, 0.71–0.91), while endometrial thickness alone gave an AUC of 0.70 (95% CI, 0.60–0.81) (difference in AUC, 0.11; 95% CI, 0.01–0.20).ConclusionThe IETA‐1 model discriminates well between benign and malignant conditions in the uterine cavity in patients without abnormal bleeding, but it overestimates the risk of malignancy. It also discriminates well between the four histological outcome categories. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Ultrasound features using MUSA terms and definitions in uterine sarcoma and leiomyoma: cohort study

ABSTRACTObjectivesTimely and accurate preoperative diagnosis of uterine sarcoma will increase patient survival. The primary aim of this study was to describe the ultrasound features of uterine sarcoma compared with those of uterine leiomyoma based on the terms and definitions of the Morphological Uterus Sonographic Assessment (MUSA) group. A secondary aim was to assess the interobserver agreement for reporting on ultrasound features according to MUSA terminology.MethodsThis was a retrospective cohort study of patients with uterine sarcoma or uterine leiomyoma treated in a single tertiary center during the periods 1997–2019 and 2016–2019, respectively. Demographic characteristics, presenting symptoms and surgical outcomes were extracted from patients' files. Ultrasound images were re‐evaluated independently by two sonologists using MUSA terms and definitions. Descriptive statistics were calculated and interobserver agreement was assessed using Cohen's κ (with squared weights) or intraclass correlation coefficient, as appropriate.ResultsA total of 107 patients were included, of whom 16 had a uterine sarcoma and 91 had a uterine leiomyoma. Abnormal uterine bleeding was the most frequent presenting symptom (69/107 (64%)). Compared with leiomyoma cases, patients with uterine sarcoma were older (median age, 65 (interquartile range (IQR), 60–70) years vs 48 (IQR, 43–52) years) and more likely to be postmenopausal (13/16 (81%) vs 15/91 (16%)). In the uterine sarcoma cohort, leiomyosarcoma was the most frequent histological type (6/16 (38%)), followed by adenosarcoma (4/16 (25%)). On ultrasound evaluation, according to Observers 1 and 2, the tumor border was irregular in most sarcomas (11/16 (69%) and 13/16 (81%) cases, respectively), but regular in most leiomyomas (65/91 (71%) and 82/91 (90%) cases, respectively). Lesion echogenicity was classified as non‐uniform in 68/91 (75%) and 51/91 (56%) leiomyomas by Observers 1 and 2, respectively, and 15/16 (94%) uterine sarcomas by both observers. More than 60% of the uterine sarcomas showed acoustic shadows (11/16 (69%) and 10/16 (63%) cases by Observers 1 and 2, respectively), whereas calcifications were reported in a small minority (0/16 (0%) and 2/16 (13%) cases by Observers 1 and 2, respectively). In uterine sarcomas, intralesional vascularity was reported as moderate to abundant in 13/16 (81%) cases by Observer 1 and 15/16 (94%) cases by Observer 2, while circumferential vascularity was scored as moderate to abundant in 6/16 (38%) by both observers. Interobserver agreement for the presence of cystic areas, calcifications, acoustic shadow, central necrosis, color score (overall, intralesional and circumferential) and maximum diameter of the lesion was moderate. The agreement for shape of lesion, tumor border and echogenicity was fair.ConclusionsA postmenopausal patient presenting with abnormal uterine bleeding and a new or growing mesenchymal mass with irregular tumor borders, moderate‐to‐abundant intralesional vascularity, cystic areas and an absence of calcifications on ultrasonography is at a higher risk of having a uterine sarcoma. Interobserver agreement for most MUSA terms and definitions is moderate. Future studies should validate the abovementioned clinical and ultrasound findings on uterine mesenchymal tumors in a prospective multicenter fashion. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Developing and validating ultrasound‐based machine‐learning models incorporating radiomics features to predict malignancy in adnexal masses

ABSTRACT Objective The primary aim of this study was to develop and internally validate ultrasound‐based radiomics models to discriminate between all types of benign and malignant adnexal masses. The secondary aim was to compare the performance of the radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model. Methods This was a retrospective, observational, single‐center study, for which all patients with an adnexal mass that were included in the ongoing International Ovarian Tumor Analysis phase‐5 and phase‐7 studies and were examined using ultrasound between January 2012 and December 2023 at Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, were eligible for inclusion. Inclusion criteria were: adnexal mass detected by ultrasound; surgical removal of the adnexal mass within 180 days after the ultrasound examination; histological confirmation of an adnexal mass; and absence of a synchronous malignant tumor. Patients without digital ultrasound images saved in DICOM format were excluded. The patient cohort was split randomly into training and validation sets using a stratified split with a ratio of 70:30, to preserve the proportion of benign and malignant cases in the two sets. Two machine‐learning models for discriminating between benign and malignant adnexal masses were built using one image per tumor, with 5‐fold cross‐validation for hyperparameter tuning, and were tested on the validation set. The variables used in model building were patient age, serum CA 125 level and the radiomics features that differed significantly between benign and malignant tumors (determined using the Mann–Whitney U ‐test with Benjamini–Hochberg correction) and were not redundant based on Pearson correlation analysis. Histology was the reference standard. We assessed the discriminative performance of the radiomics models using the area under the receiver‐operating‐characteristics curve (AUC) and classification performance using sensitivity and specificity at the optimal cut‐off of each model to classify the mass as malignant, as determined by Youden's index. The diagnostic performance of the developed radiomics models was compared with that of the ADNEX model (AUC, sensitivity and specificity at the 10% risk‐of‐malignancy cut‐off, which is the recommended threshold for clinical use of the ADNEX model). Results In total, 4501 patients met the inclusion criteria. Among these, 2428 patients were excluded owing to an absence of ultrasound images or images unsuitable for radiomics analysis. Overall, a total of 2073 patients were included in the analysis, of whom 803 (38.7%) had a histologically confirmed malignant tumor. In the validation set ( n  = 622, including 254 malignancies), the clinical–radiomics model trained using the eXtreme Gradient Boosting algorithm, including age, serum CA 125 level and 14 selected radiomics features, achieved the highest performance, with an AUC of 0.89 (95% CI, 0.86–0.92), sensitivity of 0.83 (95% CI, 0.79–0.88) and specificity of 0.81 (95% CI, 0.77–0.85) at the optimal cut‐off (31% risk of malignancy, based on Youden's index). At a 10% risk‐of‐malignancy cut‐off, it had a sensitivity of 0.94 (95% CI, 0.91–0.97) and specificity of 0.48 (95% CI, 0.42–0.53). The ADNEX model had an AUC of 0.95 (95% CI, 0.93–0.97), sensitivity of 0.97 (95% CI, 0.95–0.99) and specificity of 0.72 (95% CI, 0.68–0.77) at the 10% risk‐of‐malignancy cut‐off in the validation set. Conclusions Our results support further exploration of radiomics analysis for distinguishing between benign and malignant adnexal masses in larger study populations. Future studies should consider using multiple images per tumor and testing alternative model‐building methods, and should perform external validation to assess the generalizability of the radiomics models. © 2026 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.