Cited by 510Ovarian cancer diagnosis and treatmentBRCA gene mutations in cancerBreast Lesions and Carcinomas

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

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

Open access

Van Calster, B., Van Hoorde, K., Valentin, L., Testa, A. C., Fischerova, D., Van Holsbeke, C., Savelli, L., Franchi, D., Epstein, E., Kaijser, J., Van Belle, V., Czekierdowski, A., Guerriero, S., Fruscio, R., Lanzani, C., Scala, F., Bourne, T., Timmerman, D., International Ovarian Tumour Analysis (IOTA) group·2014·BMJ

DOI 10.1136/bmj.g5920PMC PMC4198550

Open this paper in CoChat — free account Open PDF

Abstract

OBJECTIVES: To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. DESIGN: Observational diagnostic study using prospectively collected clinical and ultrasound data. SETTING: 24 ultrasound centres in 10 countries. PARTICIPANTS: Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. MAIN OUTCOME MEASURES: Histological classification and surgical staging of the mass. RESULTS: The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. CONCLUSIONS: The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1136/bmj.g5920

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Fig 1 Calibration plots of predicted probabilities for each type of tumour. Data have been calculated using validation data (n=2403). Plots show how well the predicted probabilities (x axis) agree with observed probabilities (y axis). For perfect agreement, the calibration curve falls on the ideal diagonal line. Histograms below plots show distribution of predicted probabilitiesCC BY-NC · PubMed Central

Fig 2 Discrimination plot of ADNEX model after it was updated on pooled dataset (n=5909). For each predicted tumour type, box plots of probabilities are presented for each confirmed tumour type (reference standard). Red vertical lines show baseline probabilities for each type of tumour. For example, the baseline probability of a benign tumour is 0.681; for most women with a benign tumour the predicted probability of a benign tumour was higher than 0.9, whereas most women with an ovarian malignancy (most notably stage II-IV cancer) had clearly lower predicted probabilities of a benign tumourCC BY-NC · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Applied logistic regression
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Applied Logistic Regression
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
Cancer statistics, 2014
Siegel, R., Ma, J., Zou, Z., & Jemal, A. (2014) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21208 · Cited by 11,499
SEER Cancer Statistics Review, 1975-2003
Ries, L. A. G., Harkins, D., Krapcho, M. F., Mariotto, A. B., Miller, B., Feuer, E. J., Clegg, L. X., Eisner, M. P., Horner, M., Howlader, N., et al. (2006) · Digital Archive @ GSU · Cited by 11,404
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299
Signet-Ring Cell Carcinoma of the Colon: A Case Report and Review of the Literature
Park, P. Y., Goldin, T. A., Chang, J. C. S., Markman, M. M., & Kundranda, M. N. (2015) · Case Reports in Oncology · doi:10.1159/000441772 · Cited by 5,273
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Ovarian Cancer
Rubin, M., & Stephen, C. D. (1993) · Cited by 3,149
Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
Rao, S. J. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s263 · Cited by 1,967

Show all 57 references

Sorted by citations

Items 401–500 of 510

Recent Advances and Future Directions of Diagnostic and Prognostic Prediction Models in Ovarian Cancer
Zeng, J., Cao, W., & Wang, L. (2021) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-021-2255-y
Multyparametric MRI in differential diagnosis of epithelial ovarian tumors
Nosova, Y. V., Solopova, A. E., & Khabas, G. N. (2020) · GYNECOLOGY · doi:10.26442/20795696.2020.6.200519
“новообразований придатков (ADNEX), проведенной группой Международного анализа опухолей яичников (IOTA): математическая модель, которая рассчитывает вероятность злокачественного потенциала новообразования с высокой положительной прогностической ценностью [13, 14].”
CLINICOPATHOLOGIC FEATURES OF PROBABLY MALIGNANT ADNEXAL MASSES WITHOUT SIGNS OF ASCITES AND CARCINOMATOSIS
Çeliksoy, H. Y., Sözen, H., Baktıroğlu, M., Topuz, S., & Salihoğlu, M. Y. (2021) · Journal of Istanbul Faculty of Medicine / İstanbul Tıp Fakültesi Dergisi · doi:10.26650/iuitfd.2020.0038
“194 Clinicopathologic features of probably malignant adnexal masses İstanbul Tıp Fakültesi Dergisi • J Ist Faculty Med 2021;84(2):192-6”
BackgroundInfluential
Curva de aprendizaje para el diagnóstico ecográfico específico de masas anexiales
Guillermo Avilés, L., Cerva, H. V., Linder Díaz, C., Fernando Amor, L., Alberto Iturra, A., Mauricio León, A., Víctor Toledo, S., & Raimundo Avilés, D. (2021) · Revista chilena de obstetricia y ginecología · doi:10.4067/s0717-75262021000200137 · Cited by 1
Operatives Management von Adnextumoren
Schüler‐Toprak, S., & Ortmann, O. (2021) · Frauenheilkunde up2date · doi:10.1055/a-1372-7828
“Das ADNEX-Modell berücksichtigt zusätzlich zu sonografischen Kriterien auch klinische Parameter [19].”
Background
Imaging in Gynaecological Practice
Amso, N. N. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108582322.004
References
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021) · Wiley eBooks · doi:10.1002/9781119333784.refs5
Protocol of a Multi-Center Initiative to Create a Standardized Ovarian Ultrasound Image Database in China
Wang, J., He, P., Gao, L., Tian, Y., & Wu, Q. (2022) · Clinical and Experimental Obstetrics & Gynecology · doi:10.31083/j.ceog4904096
“The IOTA group has developed a number of diagnostic strategies, such as Easy Descriptors, Logistic Regression Models 1 and 2 (LR1 and LR2) [14], Simple Rules [15,16], Simple Rules risk assessment [17], and Assessment of Different Neoplasias in the adneXa (ADNEX) model [18].”
Method
Sonography of adnexal lesion, “ADNEX”, and ovarian cancer: clinical utility of a predictive model
Jan, Ž., & Mörtl, M. (2022) · Geburtshilfe und Frauenheilkunde · doi:10.1055/s-0042-1746159
Sonographie in der Gynäkologie - Teil 2: suspekte und pathologische Befunde
Voigt, F., Faschingbauer, F., Dammer, U., Vasku, M., Maass, N., & Goecke, T. W. (2016) · Springer eBooks · doi:10.1007/978-3-662-49480-6_8
Ovarialzyste
Kantelhardt, E. J., & Papendick, N. (2020) · Springer eBooks · doi:10.1007/978-3-662-59809-2_21
Serum free amino acid profiling in differential diagnosis of ovarian tumors – a case control study with review of the literaturePreprint
Horała, A., Plewa, S., Dereziński, P., Klupczyńska, A., Matysiak, J., Nowak‐Markwitz, E., & Kokot, Z. J. (2020) · Research Square · doi:10.21203/rs.3.rs-109758/v1
“Wiley-Blackwell; 2010 Oct;161(4):755–67.”
Adnexe
Germer, U. (2017) · Springer eBooks · doi:10.1007/978-3-662-53662-9_33
Borderline ovarian tumors: modern diagnostic criteria at the preoperative stage
Tsypurdeyeva, A. A., Protasova, A. E., Александровна, С. И., & Mikhelashvili, L. I. (2022) · Journal of obstetrics and women s diseases · doi:10.17816/jowd81798
Extramedullary haematopoiesis presenting as an adnexal mass in a patient with β-thalassaemia
Filippi, V., Reina, H., Monod, C., & Manegold‐Brauer, G. (2022) · BMJ Case Reports · doi:10.1136/bcr-2022-249422
Radiology Investigations and Interventions in Gynaeoncology
Ambadipudi, L. (2022) · Springer eBooks · doi:10.1007/978-3-030-94110-9_18
Diagnostic added-value of CA125 on O-RADS and Simple Rules in discrimination benign and malignant adnexal massesPreprint
Gong, L., Dong, S., Li, X.-Y., Wu, Y., Zhang, S., Feng, Y.-N., Du, X.-S., Tian, J., & Sun, L.-T. (2022) · Research Square · doi:10.21203/rs.3.rs-2062886/v1
Aktuelle Aspekte zur künstlichen Intelligenz in der gynäkologischen Diagnostik
Scharf, J. L., Dracopoulos, C., Gembicki, M., Welp, A., & Weichert, J. (2022) · Die Gynäkologie · doi:10.1007/s00129-022-04993-8
Editorial.
Timmerman, D. (2015) · PubMed Central
The Female Urogenital System in Geriatric Patients
Cova, M. A., Bottaro, L., Marrocchio, C., & Bozzato, A. M. (2023) · Practical issues in geriatrics · doi:10.1007/978-3-031-14877-4_11
External Validation of Simple Role Risk (SRRisk) of Malignancy in Adnexal Masses Based on IOTA Simple Rules
Mohamed, S. S. M., Elbehidy, T. A. E., Ahmed, R. A. E., & Ali, M. R. (2023) · The Egyptian Journal of Hospital Medicine · doi:10.21608/ejhm.2023.305559
“3% for a cut-off of 10% to predict malignancy ((28)).”
Background
Developing clinical prediction models for nominal polytomous outcomes: a simulation study comparing available approachesPreprint
Pate, A., Riley, R. D., Sperrin, M., CALSTER, B. V., Sergeant, J. C., Peek, N. B., van Staa, T. P., Mamas, M. A., Lip, G. Y. H., O’Flaherty, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3121017/v1
Development and External Validation of Partial Proportional Odds Risk Prediction Models for Cancer Stage at Diagnosis among Males and Females in Canada
Biziaev, T., Aktary, M. L., Wang, Q., Chekouo, T., Bhatti, P., Shack, L., Robson, P. J., & Kopciuk, K. A. (2023) · Cancers · doi:10.3390/cancers15143545 · Cited by 1
Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithmPreprint
Ledger, A., Ceusters, J., Lil, V., Antonia, T., Holsbeke, C. V., Dorella, F., Tom, B., Froyman, W., Dirk, T., & CALSTER, B. V. (2023) · medRxiv · doi:10.1101/2023.07.25.23293141
Evaluation of the incorporation of an IOTA-ADNEX model in the discrimination of adnexal masses in our third-level hospital centre, taking into account the menopausal status of patients. Five years of experience
Hernández, N. E., Escudero, J. F. D. L., Méndez, L. I. P., Quintana, D. R. B., Santana, E. B., Álvarez, J. A. P., Medina, N. R. S., Pérez, C. C., & Lopez-Fe, A. Q. (2023) · Clínica e Investigación en Ginecología y Obstetricia · doi:10.1016/j.gine.2023.100910
Assessing the diagnostic performance of four ovarian malignancy prediction risk models in differentiating benign and malignant ovarian masses in a tertiary hospital
Sarmiento-Babiera, M. J. F., & Llamas-Clark, E. F. (2022) · Philippine Journal of Obstetrics and Gynecology · doi:10.4103/pjog.pjog_37_22
Beyond the AJR: Augmenting Adnexal Mass Evaluation Through Standardized Risk Models
Virarkar, M., & Bhosale, P. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.30052
Borderline Ovarian Tumours: Early and Advance Stage: Serous, Mucinous, Micro Invasive Cancer, Invasive Implants
Singh, K., & Gupta, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-36179-1_10
Uso de modelos preditivos de malignidade por ressonância magnética, ultrassonografia e biomarcardores séricos na diferenciação das massas anexiais
Pereira, P. N. (2021) · doi:10.47749/t/unicamp.2021.1253434
Evaluation of American College of Radiology Ovarian‐Adnexal Reporting and Data System ultrasound to predict malignancy risk in adnexal lesions
Li, Y., Shao, G., Wu, M., Zhang, F., Zhang, Y., & Shao, C. (2023) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.15831
Applicabilité de l’Adnex score dans la prédiction de la malignité des kystes ovariens
Mekni, K., Baba, M., Haddad, I., Aaraar, M., Mejri, O., & ElFekih, C. (2023) · Gynécologie Obstétrique Fertilité & Sénologie · doi:10.1016/j.gofs.2023.12.001
Uterus and Ovarian Imaging
Horta, M., & Cunha, T. M. (2023) · Springer eBooks · doi:10.1007/978-3-031-28524-0_17
Discussion of the importance of ultrasound for the preoperative diagnosis of ovarian tumors (the main provisions of the consensus of leading international organizations, published in June 2021)
Чекалова, М. А., Кедрова, А. Г., Cherkasov, E. Y., Kryazheva, V. S., & Greyan, T. A. (2023) · Tumors of female reproductive system · doi:10.17650/1994-4098-2023-19-4-114-122
Authors requested withdrawal before official publication_Performance of the IOTA ADNEX model in preoperative assessment of suspicious adnexal masses with high-risk of malignancy index in Alexandria University Gynecology Oncology Center: a prospective study
El‐Tawab, S. S., Hamed, A. S., Tawfik, T. A. S., & Youssef, R. E. N. E. (2024) · Italian Journal of Gynaecology and Obstetrics · doi:10.36129/jog.2024.158
Differential Diagnosis and Detection of Malignancy of Ovarian Tumors
Kurjak, A., & Petanovski, Z. (2024) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-2010
Automatic segmentation of malignant and benign adnexal lesions on ultrasound scans
Whitney, H. M., Yoeli‐Bik, R., Abramowicz, J. S., Li, L., Li, H., Longman, R. E., Lengyel, E. R., & Giger, M. L. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3008273
Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis
Lourenço, M., Arrufat, T., Satorres-Pérez, E., Maderuelo, S., Álamo, B. N.-D., Guerriero, S., Orozco, R., & Alcázar, J. L. (2024) · Applied Sciences · doi:10.3390/app14072998
Developing the ‘OCRAT’ Progressive Web Application (PWAs) for assessing ovarian cancer risk strategies
Nguyen, H. B., Tran, D. T., & Nguyễn, V. Q. H. (2024) · Tạp chí Y Dược Huế · doi:10.34071/jmp.2024.2.5
Performance of Pre-Operative IOTA Three-Step Algorithm in Detecting Ovarian Carcinoma in a Referral Center in Indonesia
Kurniadi, A., Permadi, W., Kusuma, A. Y., Kireina, J., Andarini, M. Y., Winarno, G. N. A., & Harsono, A. B. (2024) · Indonesian Journal of Cancer · doi:10.33371/ijoc.v18i1.1044
The role of prediction models in the classification of adnexal mass
Vilendečić, Z., & Stefanović, A. (2024) · Medicinski podmladak · doi:10.5937/mp75-43210
Características ultrasonográficas del disgerminoma, a propósito de 3 casos
Paredes, R. O. L. (2024) · Revista Peruana de Ginecología y Obstetricia · doi:10.31403/rpgo.v70i2634
Ultrasound Characterization of Ovarian Pathology: Diagnostic Performance and Predictive Value of Specific Sonographic Features
Bhaskar, S., Pasoria, S., & Manmohan, M. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24613151035
Diagnostic accuracy of risk prediction models in postmenopausal patients with suspected ovarian cancer
Taşkıran, Ç., & Cantürk, M. M. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00450-9
Diagnostic rentability of IOTA models for differentiating between benign and malignant complex adnexal masses
Pérez, A. R., Caruso, A., Garrido, M. P., Jiménez, I. R., Velasco, A. P., & Alba, J. J. F. (2024) · Clínica e Investigación en Ginecología y Obstetricia · doi:10.1016/j.gine.2024.101000
Head-to-head comparison of the RMI and ADNEX models to estimate the risk of ovarian malignancy: systematic review and meta-analysis of external validation studiesPreprint
Barreñada, L., Ledger, A., Kotlarz, A., Dhiman, P., Collins, P. G. S., Wynants, L., Verbakel, J. Y. J., Valentin, L., Timmerman, D., & CALSTER, B. V. (2024) · medRxiv · doi:10.1101/2024.11.29.24318146
Sonografische Differenzialdiagnostik von Adnexbefunden
Hoopmann, M. (2024) · gynäkologie + geburtshilfe · doi:10.1007/s15013-024-5955-1
“Sowohl die IOTA-Ausgangsarbeit wie auch zahlreiche Bestätigungsstudien do-kumentieren eine gut funktionierende Unterscheidung zwischen benignen und bösartigen Befunden [19, 20, 21].”
External validation of ultrasound-based models for discrimination between benign and malignant adnexal masses in Italy: the prospective multicenter IOTA phase 6 studyPreprint
Moro, F., Momi, M., Bertoldo, V., Ledger, A., Barreñada, L., Ceusters, J., Sturla, D., Ghezzi, F., Mor, E., Fornari, L., et al. (2024) · medRxiv · doi:10.1101/2024.12.23.24319517 · Cited by 1
Imaging Considerations for Adnexal Masses
Chawla, T. P. (2024) · Canadian Women s Health Today · doi:10.58931/cwht.2024.1315
Mixed germ cell tumor presenting with mixed sonographic appearance and unique clinical presentation
Dumont, S., Amant, F. C., Froyman, W., Timmerman, D., Rompuy, A. V., & den Bosch, T. V. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29179
Correlation between clinical presentation with USG findings and per-operative findings of ovarian tumor
Dutta, L., Hoque, N. J., Chowdhury, R. A., & Nahar, N. (2025) · International Journal of Reproduction Contraception Obstetrics and Gynecology · doi:10.18203/2320-1770.ijrcog20250163
Adnexe
Germer, U., & Hoopmann, M. (2025) · Springer eBooks · doi:10.1007/978-3-662-67373-7_35
Echoscopie van het adnex
Geomini, P. M. A. J., Froyman, W., & Dreyer, K. (2024) · doi:10.1007/978-90-368-3035-5_5
Sonografische Differenzialdiagnostik von Adnexbefunden
Hoopmann, M. (2025) · Im Focus Onkologie · doi:10.1007/s15015-025-4190-5
Ovarian Malignance Risk Prediction from Medical Notes: a case study in Vietnam
Bui, H.-T., Nguyen, H.-L., Pham, C.-M., Vu, H., & Tran, T.-H. (2025) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr67746.2025.11133955
Open- source computed tomography (CT)-based sarcopenia assessment in women with suspicious adnexal masses: a feasibility and reproducibility study
Badran, P. M., Stecca, C. M., de Castro, J. T. D. S., Ferreira, H. D. J., & Jales, R. M. (2025) · Clinical Radiology · doi:10.1016/j.crad.2025.107088
Quantitative Analysis of the O‐ RADS Classification Using Ultrasound Scoring Method ( USM ) and Its Application Significance
Lu, J., Chen, C., Cheng, M., Gao, Y., Xu, C., Ji, X., Fan, J., Huang, W., Sun, L., & Zhao, S. (2025) · Journal of Clinical Ultrasound · doi:10.1002/jcu.70092
Preoperative Assessment of Surgical Resectability in Ovarian Cancer Using Ultrasound: A Narrative Review Based on the ISAAC Trial
Alcázar, J. L., Morales, C., Venturo, C., de la Maza, F., Lucio, L., Lozano, M., Jiménez, J. C. V., Orozco, R., & Ludovisi, M. (2025) · Onco · doi:10.3390/onco5040046
Identifying the best diagnostic test for Ovarian cancer in premenopausal women with non-specific symptoms – results from the ROCkeTS prospective, multicentre, cohort studyPreprint
Sundar, S. S., Agarwal, R., Scandrett, K., Davenport, C., CALSTER, B. V., Johnson, S., Sengupta, P., Selvi-Vikram, R., Kwong, F. L., Mallett, S., et al. (2025) · medRxiv · doi:10.1101/2025.10.17.25338220
Functional ovarian disease
Bharwani, N., Mira, A. R., & Jeyarajah, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27354-4.00007-7
Case Report: Strumal carcinoid tumor in the ovary: report of a rare occurrence with a review of literature
Zhu, W., Xu, Y., & Yao, Q. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1685641
A Population-Specific Ensemble Machine Learning Model for Predicting Borderline or Malignancy Risk of Ovarian Masses in Macao: A Multicenter Retrospective Study
Chio, C.-F., Sin, L.-F., Loi, H.-S., Cheang, H.-K., Chan, I., Hong, S., Fong, W. I., Chan, K. A., & Wong, S. (2025) · Clinical Medicine Insights Oncology · doi:10.1177/11795549251388312
Giant ovarian cyst mimicking no-weight loss after sleeve gastrectomy
D’Amato, G., Musella, M., Bartolini, C., Borrelli, L., D’Ambrosio, A., Franzese, A., Schiavone, V., Bellantone, C., Caricato, C., & Ingenito, M. (2025) · Journal of Surgical Case Reports · doi:10.1093/jscr/rjaf872
“The application of the IOTA criteria (Simple Rules, ADNEX model) achiev es an accuracy > 90% in the benign/malignant distinction [11].”
Mixed-methods evaluation of the implementation of IOTA-ADNEX ultrasound triage in NHS secondary care ovarian diagnostic one-stop clinics
Do, V., Crisp, H., Cummins, C., Kannangara, S., Smotra, G., Tarbuck, B., Duke, O. F., Salar, A., Jhita, N., Sai, V., et al. (2026) · BMJ Open Quality · doi:10.1136/bmjoq-2025-003909
Rethinking Adnexal Mass Diagnosis with Dynamic Contrast-enhanced US and Deep Learning
Huber, T. S., & Adams, L. C. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.250977
Diagnostic Accuracy of O‐ RADS US , IOTA Simple Rules, Subjective Assessment, and the Sequential IOTA Simple Rules–Subjective Assessment ( SR – SA ) Strategy in Adnexal Masses: High Sensitivity Versus the Challenge of Specificity in a Middle‐Resource Setting
Stecca, C. M., Badran, P. M., Mazzer, M. F. G., & Jales, R. M. (2025) · Sonography · doi:10.1002/sono.70029
Additional file 4 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704227
Additional file 1 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25572533
Additional file 2 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25572654
Additional file 1 of Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
Ledger, A., Ceusters, J., Valentin, L., Antonia, T., Van Holsbeke, C., Franchi, D., Bourne, T., Froyman, W., Timmerman, D., & CALSTER, B. V. (2023) · Figshare · doi:10.6084/m9.figshare.24633313
Additional file 6 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704233
Additional file 3 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704224
Additional file 1 of Adaptive sample size determination for the development of clinical prediction models
Christodoulou, E., van Smeden, M., Edlinger, M., Timmerman, D., Wanitschek, M. M., Steyerberg, E. W., & CALSTER, B. V. (2021) · Figshare · doi:10.6084/m9.figshare.14256821
Additional file 5 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704230
Correlation between ultrasound and gross pathology examination of malignant ovarian tumors: a pictorial review for pattern recognition
Taliento, C., Nieuwenhuysen, E. V., Labie, P., Rompuy, A. V., Timmerman, D., & Froyman, W. (2026) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2026.104618
The diagnostic puzzle: Meigs' syndrome, pseudo‐Meigs' syndrome and ovarian cancer
Moro, F., Pellecchia, G., Perrone, F., D’Alessandris, N., Fagotti, A., & Testa, A. C. (2026) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.70195
Enhanced ovarian cancer diagnosis using deep learning on pelvic ultrasound with integrated clinical data: retrospective multicenter study
Jeong, S., Cho, H., Kim, J. S., Lee, S., Song, J. Y., Hong, J. H., Lee, J. K., Kim, S. K., & Cho, H.-W. (2026) · Journal of Gynecologic Oncology · doi:10.3802/jgo.2026.37.e84
“Presence of solid component is one of the well-known important predictors of a malignant ovarian mass [9,25,30].”
Diagnostischer Einsatz bei Adnextumoren
Lengyel, D. (2026) · Springer eBooks · doi:10.1007/978-3-662-71396-9_27
Clinical use of the IOTA terminology in routine clinical practice and its impact on diagnostic performance and calibration of the ADNEX model and two-step strategy: A Danish prospective multicenter cohort study
Karlsen, N. M. S., Sakse, A., Høgdall, C. K., Høgdall, E. V. S., Karlsen, M. A., Gerds, T. A., Andrésdóttir, G., Thuesen, L. L., Forstholm, M. M., & Dreisler, E. (2026) · Journal of Gynecology Obstetrics and Human Reproduction · doi:10.1016/j.jogoh.2026.103193
Evaluation of the IOTA ADNEX Model, Two‐Step Strategy and RMI in Routine Gynaecologic Care in Denmark and Implications for Implementation: A Prospective Multicenter Cohort Study
Karlsen, N. M. S., Dreisler, E., Høgdall, C. K., Høgdall, E. V. S., Karlsen, M. A., Gerds, T. A., Andrésdóttir, G., Thuesen, L. L., Forstholm, M. M., & Sakse, A. (2026) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.70253
Development of a novel multimodal deep learning approach to improve diagnostic precision in ovarian cancer
Chiu, P.-C., Lee, C., Hsu, H., Tai, Y., Chiang, Y., & Lu, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag224
“…five benign and five malignant image features [11], and the Assessment of Different Neo-plasia in the AdneXa (ADNEX) model (https://www.evidencio.com/ models/show/946) integrates ultrasound features with clinical factors such as age [12] and CA-125 levels for risk c alculation of malignancy [13].”
Comparison of dagnostic performance between senior and junior radiologists using subjective assessment vs ovarian-adnexal reporting and data system
Zhang, H.-P., Zhu, Q., Wu, J.-J., Yang, A.-Q., Chen, T.-T., Zhang, J., & Zhou, Y. (2026) · World Journal of Clinical Oncology · doi:10.5306/wjco.v17.i5.119512
Study protocol: analysis of gene expression profiles in peripheral blood and tumor tissue of Colombian patients diagnosed with epithelial ovarian cancer
Vargas, E., Torres, W., & Riaño-Moreno, J. C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1839127
Morphological Features in Transabdominal and Transvaginal Ultrasonography for the Assessment of Adnexal Masses: A Cross-Sectional Diagnostic Accuracy Study
Sultana, N., Sultana, J., Nasrin, S., Parvin, S., & Akter, M. J. (2025) · Islami Bank Medical College Journal · doi:10.70945/ibmcj.v020i01.056
Construction and validation of a preoperative malignancy risk prediction model for ovarian-adnexal masses based on clinical and ultrasonographic features
Zhang, L., Xiao, L., Yang, H., & Wang, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1779209
“However, apart from the ADNEX model, which incorporates three clinical indicators—patient age, serum CA125 level, and type of medical center—most models still rely solely on ultrasound imaging features (23–25), resulting in certain limitations for their broad clinical application (26).”
External Validation of the IOTA Two-Step Strategy for Pre-operative Characterization of Adnexal Masses: Comparison with ADNEX, O-RADS and a Sequential Simple Rules–ADNEX Approach
Marchetti, M., Spagnol, G., Panizzolo, E., Carollo, M., Vezzaro, T., Ferrari, J., Vio, C., Munerol, C., Tozzi, R., & Noventa, M. (2026) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2026.104824 · Cited by 1
O-RADS for assessment of adnexal lesions: current status, challenges and future directions
Guo, Y., Strachowski, L. M., Shinagare, A. B., Maturen, K. E., Patel, M. D., Sadowski, E. A., & Andreotti, R. F. (2026) · Cancer Imaging · doi:10.1186/s40644-026-01061-5
Updated structured report for dynamic ultrasonography in patients with suspected or known endometriosis: Recommendations of the international society for gynecologic endoscopy (ISGE)
Djokovic, D., de Mattos, L. A., Keckstein, J., Kotlarz, A., Pinto, P., Reis, I. S., Linnamägi, A., Su, N., Gong, X., Laganà, A. S., et al. (2026) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2026.115267
Training-only ultrasound-specific augmentation for ovarian tumor segmentation across B-mode and contrast-enhanced ultrasound
Wang, Y., Hu, L., Xue, Z., Li, Q., Li, J., & Kong, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1878351
Comparing and combining O-RADS ultrasound, serum CA125, and HE4 for the preoperative diagnosis of ovarian tumors
Gong, J., Li, K., Liu, Y., & Ren, M. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02560-4
Diagnostic Value of O‐ RADS , CA125 , and Ultra Microangiography in Differentiating Benign and Malignant Adnexal Masses
Liu, M., Kong, Q., Su, E., Zhang, Q., Zhang, J.-Y., & Chen, P. (2026) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70369
Refined risk stratification with O-RADS ultrasound v2022: Diagnostic performance compared with v2019 and the ADNEX model
Wei, X., Yang, W., Li, F., Chang, L., Wang, L., Shi, C., Zhang, T., Zhu, J., & Wei, X. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113102
Exploratory evaluation of a CEUS-derived substitution for solid-component measurement in the ADNEX model
Wu, M., Chen, Y., Wu, S. S., Wang, R. J., Meng, X., Wen, H., Zhang, R., Mu, L., Xiao, L., Liu, T., et al. (2026) · BMC Women s Health · doi:10.1186/s12905-026-04700-y
Fertility-sparing surgery for ovarian clear cell carcinoma
Heredia, F. M., Pamela Villalobos, H., Galindo, P., Paredes, C., & Escalona, J. R. (2026) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2026.104895
Adnexal Masses
Porcayo, M. A. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-13454-7_34
Exosome-Associated Proteins as Mediators and Biomarkers of Ovarian Cancer Dissemination
Shefer, A., Dzhugashvili, E., Чернышова, А. Л., & TAMKOVICH, S. N. (2026) · Biomolecules · doi:10.3390/biom16081150
“In addition, the IOTA Assessment of Different NEoplasias in the adneXa (ADNEX) model combines clinical variables with ultrasound features to estimate the probabilities of a benign tumor, a borderline tumor, stage I invasive cancer, stage II–IV invasive cancer, or a secondary metastatic tumor [11].”
Efficient Variance Estimation for the Polytomous Discrimination Index
Feng, Q., Jia, T., Liu, P., CALSTER, B. V., & Li, J. (2026) · Statistics in Medicine · doi:10.1002/sim.70690
Predictive Modeling for Ovarian Cancer Diagnosis: Integration of Positron Emission Tomography/Computed Tomography (PET/CT) Metabolic Parameters, Clinical Risk Factors, and Serum Biomarkers
Almomani, S. Z., Almasaleha, A., Alfshikat, M., AL-Smadi, A. A., Alrawashdeh, M., Tarawneh, B. A., Sabayleh, M. A., & Al-Sharaia, I. (2026) · Cureus · doi:10.7759/cureus.114954
“These findings address key limitations of standalone imaging and biomarker testing, particularly the variable performance across different histologic subtypes and the persistent challenge of false positives [6,8].”
Changes in Ultrasound-Based Risk Stratification and Surgical Selection of Adnexal Masses During the COVID-19 Pandemic: A Single-Center Retrospective Study
Erdodi, B., Szöllösi, G. J., Torok, F., Váradi, L., Krasznai, Z. T., & Jakab, A. (2026) · Diagnostics · doi:10.3390/diagnostics16172719
“…three clinical variables (patient age, oncology center status, and CA125 value) together with six ultrasound variables (maximum lesion diameter, maximum diameter of the solid component, presence of more than 10 locules, acoustic shadows, number of papillary projections, and ascites) [10].”
Influential
Risk-Adapted Surveillance in Borderline Ovarian Tumours (BOTs): The “Barts” Evidence-Based Framework
Lekka, S., Haran, S. S., Seksaf, N. A., Arora, I., Jeyarajah, A., Phadnis, S., Lawrence, A. C., Brockbank, E. C., Manchanda, R., & Sideris, M. C. (2026) · Cancers · doi:10.3390/cancers18172764
“Standardised ultrasound reporting [34–36] is the principal prerequisite for delivering this safely outside a tertiary centre.”
Deep Learning–Based Ultrasound Screening Pipeline for the Diagnosis of Ovarian Masses: A Retrospective Multicenter Validation Study
Alvarez, M., Herrera, S., Cruz, D., & Castro, A. (2026) · Interdisciplinary Research in Medical Sciences Specialty · doi:10.51847/csbeqzi731

Authors: Van Calster, B., Van Hoorde, K., Valentin, L., Testa, A. C., Fischerova, D., Van Holsbeke, C., Savelli, L., Franchi, D., Epstein, E., Kaijser, J., Van Belle, V., Czekierdowski, A., Guerriero, S., Fruscio, R., Lanzani, C., Scala, F., Bourne, T., Timmerman, D., & International Ovarian Tumour Analysis (IOTA) group
Affiliations: Lund University (SE)
Università Cattolica del Sacro Cuore (IT)
IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
European Institute of Oncology (IT)
Karolinska University Hospital (SE)
Universitair Ziekenhuis Leuven (BE)
Medical University of Lublin (PL)
Azienda Ospedaliero-Universitaria Cagliari (IT)
University of Milan (IT)
Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
ORCID: Valentin, L.
Testa, A. C.
Savelli, L.
Franchi, D.
Epstein, E.
Kaijser, J.
Czekierdowski, A.
Guerriero, S.
Fruscio, R.
Bourne, T.
Timmerman, D.
Venue: BMJ
Publisher: BMJ
Published: 2014-10-15
Volume: 349
Issue: oct07 3
Pages: g5920-g5920
ISSN: 1756-1833 (ISSN-L)
DOI: 10.1136/bmj.g5920
PMC: PMC4198550
OpenAlex: W2099878111
Cited by: 510
References: 57
Citations by year: 2026: 52 · 2025: 60 · 2024: 70 · 2023: 54 · 2022: 55 · 2021: 59 · 2020: 36 · 2019: 24 · 2018: 30 · 2017: 19 · 2016: 27 · 2015: 19 · 2014: 4
FWCI: 27.87
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: BMJ (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
BOA (University of Milano-Bicocca) (submittedVersion, cc-by-nc)
Lirias (KU Leuven) (submittedVersion, cc-by-nc)
UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
Lund University Publications (Lund University) (submittedVersion, other-oa)
HighWire Press Open Archive (submittedVersion)
PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by-nc)
Spiral (Imperial College London) (submittedVersion, other-oa)
Funders: National Institute for Health and Care Research
Imperial College London
Imperial College Healthcare NHS Trust
Fonds Wetenschappelijk Onderzoek (G049312N)
iMinds
Malmö Högskola
Skånes universitetssjukhus
Vlaamse regering (G049312N)
Medical Research Council
Topics: Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
Keywords: ovarian cancer, ADNEX model, preoperative risk prediction, ovarian ultrasonography, borderline ovarian tumors, CA125, surgical staging, adnexal mass, triage
MeSH terms: Adnexal Diseases, Adult, Female, Humans, Neoplasm Staging, Ovarian Neoplasms, Predictive Value of Tests, Prospective Studies, Ultrasonography, Models, Statistical, Risk Assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks