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.

Department of Biomedical Data Sciences Leiden University Medical Center Leiden the Netherlands

Department of Development and Regeneration KU Leuven Leuven Belgium

Department of Gynecology AZ Delta Roeselare Belgium

Department of Human Genetics University Hospitals Leuven KU Leuven Leuven Belgium

Dipartimento Scienze della Salute della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A Gemelli IRCCS Rome Italy

Dipartimento Universitario Scienze della Vita e Sanità Pubblica Università Cattolica del Sacro Cuore Rome Italy

Division of Gynecological Oncology University Hospitals Leuven Leuven Belgium

Gynecologic Oncology Centre Department of Gynecology Obstetrics and Neonatology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

KU Leuven VIB Center for Cancer Biology Lab of Translational Genetics Leuven Belgium

Laboratory of Tumor Immunology and Immunotherapy Department of Oncology Leuven Cancer Institute KU Leuven Leuven Belgium

Leuven Cancer Institute KU Leuven Leuven Belgium

Leuven Unit for Health Technology Assessment Research KU Leuven Leuven Belgium

Queen Charlotte's and Chelsea Hospital Imperial College London London UK

See more in PubMed

American College of Obstetricians and Gynecologists. ACOG Practice Bulletin No. 83: management of adnexal masses. Obstet Gynecol. 2007;110(1):201‐214.

Van Calster B, Van Hoorde K, Valentin L, et al. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study. BMJ. 2014;349:g5920.

Van Calster B, Valentin L, Froyman W, et al. Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study. BMJ. 2020;370:m2614.

Barreñada L, Ledger A, Dhiman P, et al. ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta‐analysis of external validation studies. BMJ Med. 2024;3(1):e000817.

Amant F, Verheecke M, Wlodarska I, et al. Presymptomatic identification of cancers in pregnant women during noninvasive prenatal testing. JAMA Oncol. 2015;1(6):814.

Sun K, Jiang P, Chan KCA, et al. Plasma DNA tissue mapping by genome‐wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci. 2015;112(40):E5503.

Moss J, Magenheim J, Neiman D, et al. Comprehensive human cell‐type methylation atlas reveals origins of circulating cell‐free DNA in health and disease. Nat Commun. 2018;9(1):5068.

Kang S, Li Q, Chen Q, et al. CancerLocator: non‐invasive cancer diagnosis and tissue‐of‐origin prediction using methylation profiles of cell‐free DNA. Genome Biol. 2017;18(1):53.

Li W, Li Q, Kang S, et al. CancerDetector: ultrasensitive and non‐invasive cancer detection at the resolution of individual reads using cell‐free DNA methylation sequencing data. Nucl Acids Res. 2018;46(15):e89.

Guo S, Diep D, Plongthongkum N, Fung HL, Zhang K, Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue‐of‐origin mapping from plasma DNA. Nat Genet. 2017;49(4):635‐642.

van der Pol Y, Mouliere F. Toward the early detection of cancer by decoding the epigenetic and environmental fingerprints of cell‐free DNA. Cancer Cell. 2019;36(4):350‐368.

Che H, Jatsenko T, Lenaerts L, et al. Pan‐cancer detection and typing by mining patterns in large genome‐wide cell‐free DNA sequencing datasets. Clin Chem. 2022;68(9):1164‐1176.

Vanderstichele A, Busschaert P, Smeets D, et al. Chromosomal instability in cell‐free DNA as a highly specific biomarker for detection of ovarian cancer in women with adnexal masses. Clin Cancer Res. 2017;23(9):2223‐2231.

Vanderstichele A, Busschaert P, Landolfo C, et al. Nucleosome footprinting in plasma cell‐free DNA for the pre‐surgical diagnosis of ovarian cancer. NPJ Genom Med. 2022;7(1):30.

Timmerman D, Valentin L, Bourne TH, Collins WP, Verrelst H, Vergote I. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group. Ultrasound in Obstet Gynecol. 2000;16(5):500‐505.

Coosemans A, Baert T, Ceusters J, et al. Myeloid‐derived suppressor cells at diagnosis may discriminate between benign and malignant ovarian tumors. Int J Gynecol Cancer. 2019;29(9):1381‐1388.

Landolfo C, Achten ETL, Ceusters J, et al. Assessment of protein biomarkers for preoperative differential diagnosis between benign and malignant ovarian tumors. Gynecol Oncol. 2020;159(3):811‐819.

Landolfo C, Ceusters J, Valentin L, et al. Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery. Br J Cancer. 2024;130(6):934‐940.

Coosemans A, Ceusters J, Landolfo C, et al. Added value of serum proteins to clinical and ultrasound information in predicting the risk of malignancy in ovarian tumors. medRxiv. 2024. doi:10.1101/2024.03.14.24304282

Sauerbrei W, Taube SE, McShane LM, Cavenagh MM, Altman DG. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): an abridged explanation and elaboration. J Natl Cancer Inst. 2018;110(8):803‐811.

Installé AJ, Van den Bosch T, De Moor B, Timmerman D. Clinical data miner: an electronic case report form system with integrated data preprocessing and machine‐learning libraries supporting clinical diagnostic model research. JMIR Med Inform. 2014;2(2):e28.

Bayindir B, Dehaspe L, Brison N, et al. Noninvasive prenatal testing using a novel analysis pipeline to screen for all autosomal fetal aneuploidies improves pregnancy management. Eur J Hum Genet. 2015;23(10):1286‐1293.

Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754‐1760.

Li H, Handsaker B, Wysoker A, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078‐2079.

Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell‐free DNA comprises an in vivo nucleosome footprint that informs its tissues‐of‐origin. Cell. 2016;164(1‐2):57‐68.

Qin G, Hotilovac L. Comparison of non‐parametric confidence intervals for the area under the ROC curve of a continuous‐scale diagnostic test. Stat Methods Med Res. 2008;17(2):207‐221.

Harrell FE. Regression modeling strategies. Bios. 2018;2017(330):14.

Steyerberg EW, Harrell FE, Borsboom GJJM, Eijkemans MJC, Vergouwe Y, Habbema JDF. Internal validation of predictive models. J Clin Epidemiol. 2001;54(8):774‐781.

Demler OV, Pencina MJ, D'Agostino RB. Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012;31(23):2577‐2587.

Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ. 2016;352:i6.

Steyerberg EW, Pencina MJ, Lingsma HF, Kattan MW, Vickers AJ, Van Calster B. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest. 2012;42(2):216‐228.

Van Calster B, Wynants L, Verbeek JFM, et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol. 2018;74(6):796‐804.

R Core Team. R: A Language and Environment for Statistical Computing. 2021 https://www.R‐project.org/

Li G, Zhang Y, Li K, et al. Transformer‐based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers. Cell Rep Med. 2024;5(8):101666.

Gaillard DHK, Lof P, Sistermans EA, et al. Evaluating the effectiveness of pre‐operative diagnosis of ovarian cancer using minimally invasive liquid biopsies by combining serum human epididymis protein 4 and cell‐free DNA in patients with an ovarian mass. Int J Gynecol Cancer. 2024;34(5):713‐721.

Buckley DN, Lewinger JP, Gooden G, et al. OvaPrint—a cell‐free DNA methylation liquid biopsy for the risk assessment of high‐grade serous ovarian cancer. Clin Cancer Res. 2023;29(24):5196‐5206.

Chen L, Ma R, Luo C, et al. Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell‐free DNA. Transl Res. 2023;262:12‐24.

Zhou H, Zhang X, Liu Q, et al. Can circulating cell free DNA be a promising marker in ovarian cancer? – a genome‐scale profiling study in a single institution. J Ovarian Res. 2023;16(1):11.

Medina JE, Annapragada AV, Lof P, et al. Early detection of ovarian cancer using cell‐free DNA fragmentomes and protein biomarkers. Cancer Discov. 2025;15(1):105‐118.

Lampignano R, Neumann MHD, Weber S, et al. Multicenter evaluation of circulating cell‐free DNA extraction and downstream analyses for the development of standardized (pre)analytical work flows. Clin Chem. 2020;66(1):149‐160.

Im YR, Tsui DWY, Diaz LA, Wan JCM. Next‐generation liquid biopsies: embracing data science in oncology. Trends Cancer. 2021;7(4):283‐292.

Wan JCM, Stephens D, Luo L, et al. Genome‐wide mutational signatures in low‐coverage whole genome sequencing of cell‐free DNA. Nat Commun. 2022;13(1):4953.

Moser T, Kühberger S, Lazzeri I, Vlachos G, Heitzer E. Bridging biological cfDNA features and machine learning approaches. Trends Genet. 2023;39(4):285‐307.

Passemiers A, Tuveri S, Sudhakaran D, et al. MetDecode: methylation‐based deconvolution of cell‐free DNA for noninvasive multi‐cancer typing. Bioinformatics. 2024;40(9):btae522.

Heitzer E, Haque IS, Roberts CES, Speicher MR. Current and future perspectives of liquid biopsies in genomics‐driven oncology. Nat Rev Genet. 2019;20(2):71‐88.

Parkinson CA, Gale D, Piskorz AM, et al. Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high‐grade serous ovarian carcinoma: a retrospective study. PLoS Med. 2016;13(12):e1002198.

Newman AM, Bratman SV, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20(5):548‐554.

Chen X, Gole J, Gore A, et al. Non‐invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat Commun. 2020;11(1):3475.

Vavoulis DV, Cutts A, Thota N, et al. Multimodal cell‐free DNA whole‐genome TAPS is sensitive and reveals specific cancer signals. Nat Commun. 2025;16(1):430.

Nguyen THH, Vu GH, Nguyen TT, et al. Combination of hotspot mutations with methylation and fragmentomic profiles to enhance multi‐cancer early detection. Cancer Med. 2025;14(1):e70575.