Jolien Ceusters

Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery

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). 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. 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%. ADNEX had better ability to discriminate between benign and malignant adnexal tumours and higher clinical utility than ROMA. clinicaltrials.gov NCT01698632 and NCT02847832.

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  &lt; 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.

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.

Prospective Validation and Comparison of Different Ultrasound Methods for Discrimination Between Benign and Malignant Ovarian/Tubal Masses Prior to Surgery

The ability of different methods to discriminate between benign and malignant adnexal masses has been compared in a meta-analysis showing that the IOTA Simple Rules and the IOTA logistic regression model LR2 were superior to RMI and to all other methods for predicting malignancy in an adnexal mass included in the meta-analysis. However, a fair comparison of methods requires them to be applied on the same tumor population. The general objective of this study is to prospectively validate the Simple Rules, ADNEX, the Simple Rules risk model, LR2, and RMI on a large multicenter dataset to allow direct comparison of these tools. IOTA7 is an international multicenter prospective observational study including different types of ultrasound centers and examiners with different levels of ultrasound experience. In total, about 1700 adnexal masses with histological outcome will be included in IOTA 7.

International Ovarian Tumour Analysis (IOTA) Phase 5

The purpose of this study is to learn more about the appearance and behavior of benign-looking adnexal masses. * Benign-looking means that when viewed here by ultrasound it has the appearance of looking not harmful or not malignant. * Adnexal refers to the 'adnexa', the space in the female pelvis on either side of the uterus (or where the uterus used to be if you previously had a hysterectomy). The adnexa includes, but is not limited to, the ovaries and the fallopian tubes. * Masses refers to a variety of structures, including but not limited to: * ovarian cysts that are fluid filled sacs within or attached to an ovary * ovarian tumors that can be solid tissue or a combination of cysts and solid tissue * hydrosalpinges that are fluid collections in the fallopian tube Many women have what appear to be benign adnexal masses. Many times, removal of the masses with surgery is not necessary. Often surgery is performed unnecessarily, for fear that these masses could be cancer. There is not much information available for doctors to know how and when to follow these masses, or which ones will become cancer. This study will combine information from centers all around the world regarding the behavior of all types of benign adnexal masses. The aim of this study is to develop decision tools for doctors to know the best way to treat these masses in order to improve the detection of ovarian cancer while at the same time reduce the number of unnecessary operations.