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

O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee
Andreotti, R. F., Timmerman, D., Strachowski, L. M., Froyman, W., Benacerraf, B. R., Bennett, G. L., Bourne, T., Brown, D. L., Coleman, B. G., Frates, M. C., et al. (2019) · Radiology · doi:10.1148/radiol.2019191150 · Cited by 530
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
van den Goorbergh, R., van Smeden, M., Timmerman, D., & CALSTER, B. V. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac093 · Cited by 427
Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group
Timmerman, D., CALSTER, B. V., Testa, A. C., Savelli, L., Fischerová, D., Froyman, W., Wynants, L., Holsbeke, C. V., Epstein, E., Franchi, D., et al. (2016) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2016.01.007 · Cited by 337
Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis
Meys, E. M. J., Kaijser, J., Kruitwagen, R. F. P. M., Slangen, B. F. M., CALSTER, B. V., Aertgeerts, B., Verbakel, J. Y. J., Timmerman, D., & Gorp, T. V. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2016.01.007 · Cited by 281
“Someof thesehavenotbeenvalidatedextensivelybut seem promising, such as the ADNEX-model [83].”
Background
Clinical Use of Cancer Biomarkers in Epithelial Ovarian Cancer
Sölétormos, G., Duffy, M. J., Hassan, S. O. A., Verheijen, R. H. M., Tholander, B., Bast, R. C., Gaarenstroom, K. N., Sturgeon, C. M., Bonfrèr, J. M. G., Petersen, P. H., et al. (2015) · International Journal of Gynecological Cancer · doi:10.1097/igc.0000000000000586 · Cited by 255
“Assessment of Different NEoplasias in the adneXa.(33) The National Institute for Health and Care Excellence has introduced guidelines for early detection of ovarian cancer in symptomatic women for use by general practitioners.”
Three myths about risk thresholds for prediction models
on behalf of the Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Wynants, L., van Smeden, M., McLernon, D. J., Timmerman, D., Steyerberg, E. W., & Van Calster, B. (2019) · BMC Medicine · doi:10.1186/s12916-019-1425-3 · Cited by 213
“It was constructed using multinomial logistic regression and validated with more recent data and in other centers [14].”
Method
Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study
Froyman, W., Landolfo, C., Campo, B. D. C., Wynants, L., Sladkevicius, P., Testa, A. C., Holsbeke, C. V., Domali, E., Fruscio, R., Epstein, E., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30837-4 · Cited by 192
Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: A retrospective, multicentre, diagnostic study
Gao, Y., Zeng, S., Xu, X., Li, H., Yao, S., Song, K., Li, X., Chen, L., Tang, J., Xing, H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00278-8 · Cited by 185
Ovarian-Adnexal Reporting Lexicon for Ultrasound: A White Paper of the ACR Ovarian-Adnexal Reporting and Data System Committee
Andreotti, R. F., Timmerman, D., Benacerraf, B. R., Bennett, G. L., Bourne, T., Brown, D. L., Coleman, B. G., Frates, M. C., Froyman, W., Goldstein, S. R., et al. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.07.004 · Cited by 178
“- Terms developed by the IOTA Group that are strongly evidence based were incorporated as a package modified by descriptors that are IOTA and non-IOTA based.”
BackgroundInfluential
Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
Pate, A., Riley, R. D., Collins, P. G. S., van Smeden, M., CALSTER, B. V., Ensor, J., & Martin, G. P. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231151220 · Cited by 172
Risk scores to guide referral decisions for people with suspected ovarian cancer in secondary care: a systematic review and cost-effectiveness analysis
Westwood, M. E., Ramaekers, B. L. T., Lang, S., Grimm, S. E., Deshpande, S., de Kock, S., Armstrong, N., Joore, M. A., & Kleijnen, J. (2018) · Health Technology Assessment · doi:10.3310/hta22440 · Cited by 162
ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors
Timmerman, D., Planchamp, F., Bourne, T., Landolfo, C., du Bois, A., Chiva, L. M., Cibula, D., Concin, N., Fischerová, D., Froyman, W., et al. (2021) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2021-002565 · Cited by 158
“36 –61 A study aiming to validate the ADNEX model when applied by Level II examiners has confirmed that it can be used successfully by less-experienced examiners.”
Method
Laparoscopy to Predict the Result of Primary Cytoreductive Surgery in Patients With Advanced Ovarian Cancer: A Randomized Controlled Trial
Rutten, M. J., van Meurs, H. S., van de Vrie, R., Gaarenstroom, K. N., Naaktgeboren, C. A., Gorp, T. V., ter Brugge, H. G., Hofhuis, W., Schreuder, H. W. R., Arts, H. J. G., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2016.69.2962 · Cited by 145
Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses
Basha, M. A. A., Metwally, M. I., Gamil, S. A., Khater, H. M., Aly, S. A., Sammak, A. A. E., Zaitoun, M., Khattab, E. M., Azmy, T. M., Alayouty, N. A., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07143-7 · Cited by 127
Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
CALSTER, B. V., Valentin, L., Froyman, W., Landolfo, C., Ceusters, J., Testa, A. C., Wynants, L., Sladkevicius, P., Holsbeke, C. V., Domali, E., et al. (2020) · BMJ · doi:10.1136/bmj.m2614 · Cited by 133
Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods
Meys, E. M. J., Jeelof, L. S., Achten, N. M. J., Slangen, B. F. M., Lambrechts, S., Kruitwagen, R. F. P. M., & Gorp, T. V. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17225 · Cited by 111
ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors
Timmerman, D., Planchamp, F., Bourne, T., Landolfo, C., du Bois, A., Chiva, L., Cibula, D., Concin, N., Fischerova, D., Froyman, W., et al. (2021) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.23635 · Cited by 114
Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment
Chen, H., Yang, B.-W., Qian, L., Meng, Y.-S., Bai, X.-H., Hong, X.-W., He, X., Jiang, M.-J., Yuan, F., Du, Q.-W., et al. (2022) · Radiology · doi:10.1148/radiol.211367 · Cited by 110
Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses
Cao, L., Wei, M.-J., Liu, Y., Fu, J., Zhang, H., Huang, J., Pei, X., & Zhou, J. (2021) · Gynecologic Oncology · doi:10.1016/j.ygyno.2021.04.031 · Cited by 110
Performance of IOTA Simple Rules, Simple Rules risk assessment, ADNEX model and O‐RADS in differentiating between benign and malignant adnexal lesions in North American women
Hiett, A. K., Sonek, J., Guy, M. S., & Reid, T. J. A. (2021) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.24777 · Cited by 105
Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.
CALSTER, B. V., Hoorde, K. V., Froyman, W., Kaijser, J., Wynants, L., Landolfo, C., Anthoulakis, C., Vergote, I. B., Bourne, T., & Timmerman, D. (2015) · PubMed Central · Cited by 102
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112
Ovarian-Adnexal Reporting Lexicon for MRI: A White Paper of the ACR Ovarian-Adnexal Reporting and Data Systems MRI Committee
Reinhold, C., Rockall, A., Sadowski, E. A., Siegelman, E. S., Maturen, K. E., Vargas, H. A., Forstner, R., Glanc, P., Andreotti, R. F., & Thomassin‐Naggara, I. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.12.022 · Cited by 101
Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study
Sayasneh, A., Ferrara, L., Cock, B. D., Saso, S., Al‐Memar, M., Johnson, S., Kaijser, J., Carvalho, J., Husicka, R., Smith, A. A., et al. (2016) · British Journal of Cancer · doi:10.1038/bjc.2016.227 · Cited by 100
Key steps and common pitfalls in developing and validating risk models
Wynants, L., Collins, P. G. S., & CALSTER, B. V. (2016) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.14170 · Cited by 99
A Perspective on Ovarian Cancer Biomarkers: Past, Present and Yet-To-Come
Ueland, F. R. (2017) · Diagnostics · doi:10.3390/diagnostics7010014 · Cited by 90
High-throughput approaches for precision medicine in high-grade serous ovarian cancer
Govindarajan, M., Wohlmuth, C., Waas, M., Bernardini, M. Q., & Kislinger, T. (2020) · Journal of Hematology & Oncology · doi:10.1186/s13045-020-00971-6 · Cited by 89
A spline-based tool to assess and visualize the calibration of multiclass risk predictions
Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., & Van Calster, B. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.12.016 · Cited by 85
Adnexal masses during pregnancy: diagnosis, treatment, and prognosis
Cathcart, A. M., Nezhat, F. R., Emerson, J. B., Pejovic, T. B., NEZHAT, C. H., & NEZHAT, C. R. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.11.1291 · Cited by 87
External Validation of O-RADS US Risk Stratification and Management System
Hack, K., Gandhi, N., Bouchard‐Fortier, G., Chawla, T. P., Ferguson, S. E., Li, S., Kahn, D., Tyrrell, P. N., & Glanc, P. (2022) · Radiology · doi:10.1148/radiol.211868 · Cited by 80
Imaging in gynecological disease (12): clinical and ultrasound features of invasive and non‐invasive malignant serous ovarian tumors
Moro, F., Poma, C. B., Zannoni, G. F., Urbinati, A. M. V., Pasciuto, T., Ludovisi, M., Moruzzi, M. C., Carinelli, S. G., Franchi, D., Scambia, G., et al. (2017) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17414 · Cited by 75
Cyclophilin A/CD147 Interaction: A Promising Target for Anticancer Therapy
Han, J. M., & Jung, H. J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23169341 · Cited by 75
Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center
Araújo, K. G., Jales, R. M., Pereira, P. N., Yoshida, A., Andrade, L. A. L. D. A., Sarian, L. O., & Derchain, S. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.15963 · Cited by 73
Indeterminate Adnexal Cysts at US: Prevalence and Characteristics of Ovarian Cancer
Sadowski, E. A., Paroder, V., Patel-Lippmann, K. K., Robbins, J. B., Barroilhet, L. M., Maddox, E., McMahon, T., Sampene, E., Wasnik, A. P., Blaty, A. D., et al. (2018) · Radiology · doi:10.1148/radiol.2018172271 · Cited by 72
Diagnostic Performance of the Ovarian-Adnexal Reporting and Data System (O-RADS) Ultrasound Risk Score in Women in the United States
Jha, P., Gupta, A., Baran, T. M., Maturen, K. E., Patel-Lippmann, K. K., Zafar, H. M., Kamaya, A., Antil, N., Barroilhet, L. M., & Sadowski, E. A. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.16370 · Cited by 72
Risk of Malignant Ovarian Cancer Based on Ultrasonography Findings in a Large Unselected Population
Smith‐Bindman, R., Pōder, L., Johnson, E., & Miglioretti, D. L. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.5113 · Cited by 71
Update on Imaging of Ovarian Cancer
Forstner, R., Meissnitzer, M. W., & Cunha, T. M. (2016) · Current Radiology Reports · doi:10.1007/s40134-016-0157-9 · Cited by 70
“A prospective multi-center study reported that the nine predictors of this ADNEX model not only allow differentiation of benign tumors from malignant tumors, but also enable categorization in five types of adnexal tumors (benign; borderline; stage I tumors; stage II-IV and metastatic tumors) [25].”
Background
External validation of IOTA simple descriptors and simple rules for classifying adnexal masses
Alcázar, J. L., Pascual, M. A., Graupera, B., Aubà, M., Errasti, T., Olartecoechea, B., Ruiz-Zambrana, Á., Hereter, L., AJOSSA, S., & Guerriero, S. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.15854 · Cited by 67
Benign descriptors and ADNEX in two‐step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort
Landolfo, C., Bourne, T., Froyman, W., CALSTER, B. V., Ceusters, J., Testa, A. C., Wynants, L., Sladkevicius, P., Holsbeke, C. V., Domali, E., et al. (2022) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.26080 · Cited by 63
Ovarian mass–differentiating benign from malignant: the value of the International Ovarian Tumor Analysis ultrasound rules
Abramowicz, J. S., & Timmerman, D. (2017) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2017.07.019 · Cited by 65
The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study)
Chiappa, V., Bogani, G., Interlenghi, M., Salvatore, C., Bertolina, F., Sarpietro, G., Signorelli, M., Castiglioni, I., & Raspagliesi, F. (2020) · Journal of Ultrasound · doi:10.1007/s40477-020-00503-5 · Cited by 62
Systematic Review and Meta-Analysis of O-RADS Ultrasound and O-RADS MRI for Risk Assessment of Ovarian and Adnexal Lesions
Zhang, Q., Dai, X., & Li, W. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.22.28396 · Cited by 66
Ovarian Adnexal Reporting Data System (O-RADS) for Classifying Adnexal Masses: A Systematic Review and Meta-Analysis
Vara, J., Manzour, N., Chacón, E., López-Picazo, A., Linares, M., Pascual, M. A., Guerriero, S., & Alcázar, J. L. (2022) · Cancers · doi:10.3390/cancers14133151 · Cited by 66
Imaging in gynecological disease (14): clinical and ultrasound characteristics of ovarian clear cell carcinoma
Pozzati, F., Moro, F., Pasciuto, T., Gallo, C., Ciccarone, F., Franchi, D., Mancari, R., Giunchi, S., Timmerman, D., Landolfo, C., et al. (2018) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.19171 · Cited by 58
Performance of IOTA ADNEX model in evaluating adnexal masses in a gynecological oncology center in China
Chen, H., Qian, L., Jiang, M., Du, Q., Yuan, F., & Feng, W. (2019) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.20363 · Cited by 54
External Validation of the Ovarian-Adnexal Reporting and Data System ( O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups
Timmerman, S., Valentin, L., Ceusters, J., Testa, A. C., Landolfo, C., Sladkevicius, P., Holsbeke, C. V., Domali, E., Fruscio, R., Epstein, E., et al. (2022) · JAMA Oncology · doi:10.1001/jamaoncol.2022.5969 · Cited by 53
External validation of the IOTA ADNEX model performed by two independent gynecologic centers
Szubert, S., Wójtowicz, A., Moszyński, R., Żywica, P., Dyczkowski, K., Stachowiak, A., Sajdak, S., Szpurek, D., & Alcázar, J. L. (2016) · Gynecologic Oncology · doi:10.1016/j.ygyno.2016.06.020 · Cited by 53
Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors
Moro, F., Zannoni, G. F., Arciuolo, D., Pasciuto, T., Amoroso, S., Mascilini, F., Mainenti, S., Scambia, G., & Testa, A. C. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17222 · Cited by 52
Clinical Use of Programmed Cell Death-1 and Its Ligand Expression as Discriminatory and Predictive Markers in Ovarian Cancer
Chatterjee, J., Dai, W., Aziz, N. H. A., Teo, P. Y., Wahba, J., Phelps, D. L., Maine, C. J., Whilding, L. M., Dina, R. E., Trevisan, G., et al. (2016) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-2366 · Cited by 52
“95) for discriminating all-stage EOC from benign tumors (23).”
Background
Adnexal lesions: Imaging strategies for ultrasound and MR imaging
Sadowski, E. A., Rockall, A., Maturen, K. E., Robbins, J. B., & Thomassin‐Naggara, I. (2018) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2018.06.003 · Cited by 49
Development of a clinical prediction model for diagnosing adenomyosis
Tellum, T., Nygaard, S., Skovholt, E. K., Qvigstad, E., & Lieng, M. (2018) · Fertility and Sterility · doi:10.1016/j.fertnstert.2018.06.009 · Cited by 49
Clinical Utility of Risk Models to Refer Patients with Adnexal Masses to Specialized Oncology Care: Multicenter External Validation Using Decision Curve Analysis
Wynants, L., Timmerman, D., Verbakel, J. Y. J., Testa, A. C., Savelli, L., Fischerová, D., Franchi, D., Holsbeke, C. V., Epstein, E., Froyman, W., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-3248 · Cited by 48
“After the publication of the 2 systematic reviews, the ADNEX risk model (Assessment of Different Neoplasias in the adnexa) was published (8).”
BackgroundMethodInfluential
Molecular Management of High-Grade Serous Ovarian Carcinoma
Punzón-Jiménez, P., Lago, V., Domingo, S., Simón, C., & Mas, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232213777 · Cited by 49
“The Assessment of Different Neoplasias in the Adnexa (ADNEX) model represents an alternative strategy that considers six ultrasound and three clinical predictors (including CA125 levels) to discriminate between benign, borderline, invasive, and metastatic ovarian tumors [62].”
Background
Evaluation and Management of Ultrasonographically Detected Ovarian Tumors in Asymptomatic Women
van Nagell, J. R., & Miller, R. W. (2016) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000001384 · Cited by 47
“discriminate between benign and malignant ovarian tumors.(36) The following clinical, ultrasound, and biomarker predictors were evaluated: patient age, serum CA 125, maximum tumor diameter, proportion of solid tissue, cyst locules greater than 10 cm, number of papillary projections, acoustic shadows, ascites, and type…”
Background
Ovarian Preservation from Tumors and Torsions in Girls: Prospective Diagnostic Study
Stanković, Z. B., Sedlecky, K., Savic, D., Lukač, B. J., Mažibrada, I., & Perović, S. (2017) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2017.01.008 · Cited by 51
Tumor markers: myths and facts unfolded
Faria, S. C., Sagebiel, T. L., Patnana, M., Cox, V. L., Viswanathan, C., Lall, C. G., Qayyum, A., & Bhosale, P. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1845-0 · Cited by 47
British Gynaecological Cancer Society (BGCS) ovarian, tubal and primary peritoneal cancer guidelines: Recommendations for practice update 2024
Moss, E. L., Taylor, A., Andreou, A., Ang, C., Arora, R., Attygalle, A. D., Banerjee, S. N., Bowen, R. L., Buckley, L., Burbos, N., et al. (2024) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2024.06.025 · Cited by 49
Subjective ultrasound assessment, the ADNEX model and ultrasound‐guided tru‐cut biopsy to differentiate disseminated primary ovarian cancer from metastatic non‐ovarian cancer
Epstein, E., CALSTER, B. V., Timmerman, D., & Nikman, S. (2015) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.14892 · Cited by 46
Solving the problem of incomplete data in medical diagnosis via interval modeling
Wójtowicz, A., Żywica, P., Stachowiak, A., & Dyczkowski, K. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.05.029 · Cited by 45
Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting
Wynants, L., Kent, D. M., Timmerman, D., Lundquist, C. M., & CALSTER, B. V. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0046-9 · Cited by 47
“, tertiary centers versus others) [8, 89, 90].”
Validation of the Performance of International Ovarian Tumor Analysis (IOTA) Methods in the Diagnosis of Early Stage Ovarian Cancer in a Non-Screening Population
Froyman, W., Wynants, L., Landolfo, C., Bourne, T., Valentin, L., Testa, A. C., Sladkevicius, P., Franchi, D., Fischerová, D., Savelli, L., et al. (2017) · Diagnostics · doi:10.3390/diagnostics7020032 · Cited by 45
“for the discrimination of benign from all malignant disease (all malignant subtypes grouped together) [24,25,31].”
BackgroundMethodInfluential
Serum HE4 and diagnosis of ovarian cancer in postmenopausal women with adnexal masses
Gentry‐Maharaj, A., Burnell, M., Dilley, J., Ryan, A., Karpinskyj, C., Gunu, R., Mallett, S., Deeks, J. J., Campbell, S. C., Jacobs, I. J., et al. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.07.031 · Cited by 42
Methods of Assessing Ovarian Masses
Froyman, W., & Timmerman, D. (2019) · Obstetrics and Gynecology Clinics of North America · doi:10.1016/j.ogc.2019.07.003 · Cited by 49
Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women
Davenport, C. F., Rai, N., Sharma, P., Deeks, J. J., Berhane, S., Mallett, S., Saha, P., Champaneria, R., Bayliss, S. E., Snell, K. I. E., et al. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011964.pub2 · Cited by 47
ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies
Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J. Y., Timmerman, D., Valentin, L., & Van Calster, B. (2024) · BMJ Medicine · doi:10.1136/bmjmed-2023-000817 · Cited by 42
Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms
Seyedsalehi, A., & Fazel, S. (2024) · BMJ Mental Health · doi:10.1136/bmjment-2024-300990 · Cited by 45
Risk-prediction models in postmenopausal patients with symptoms of suspected ovarian cancer in the UK (ROCkeTS): a multicentre, prospective diagnostic accuracy study
Sundar, S. S., Agarwal, R., Davenport, C. F., Scandrett, K., Johnson, S., Sengupta, P. P., Vikram, R., Kwong, F. L., Mallett, S., Rick, C., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00406-6 · Cited by 41
CT Features of Ovarian Tumors: Defining Key Differences Between Serous Borderline Tumors and Low-Grade Serous Carcinomas
Nougaret, S., Lakhman, Y., Molinari, N., Feier, D. S., Scelzo, C., Vargas, H. A., Sosa, R. E., Hricak, H., Soslow, R. A., Grisham, R. N., et al. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.17.18254 · Cited by 42
“Furthermore, several risk prediction models have been put forth to discriminate between benign, borderline, and malignant ovarian masses [23, 24].”
Background
Imaging and Differential Diagnosis of Ovarian Cancer
Shetty, M. K. (2019) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2019.04.002 · Cited by 42
Unraveling a difficult diagnosis: the tricks for early recognition of ovarian cancer
Giampaolino, P., Corte, L. D., Foreste, V., Vitale, S. G., Chiofalo, B., Cianci, S., Zullo, F., & Bifulco, G. (2019) · Minerva Medica · doi:10.23736/s0026-4806.19.06086-5 · Cited by 41
Contemporary Guidelines for Adnexal Mass Imaging: A 2020 Update
Stein, E. B., Roseland, M. E., Shampain, K. L., Wasnik, A. P., & Maturen, K. E. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02812-z · Cited by 41
“The method of risk assessment used in O-RADS US is based on data from the International Ovarian Tumor Analysis (IOTA) group’s Assessment of Different Neoplasias in the adnexa (ADNEX), a mathematical model which calculates the likelihood of malignancy with high positive predictive value [27, 28].”
Method
A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study
Dunias, Z. S., CALSTER, B. V., Timmerman, D., Boulesteix, A., & van Smeden, M. (2024) · Statistics in Medicine · doi:10.1002/sim.9932 · Cited by 40
Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
Moro, F., Ciancia, M., Zaçe, D., Vagni, M., Tran, H. E., Giudice, M. T., Zoccoli, S. G., Mascilini, F., Ciccarone, F., Boldrini, L., et al. (2024) · International Journal of Cancer · doi:10.1002/ijc.35092 · Cited by 54
Accuracy of ultrasonography performed by examiners with varied training and experience in predicting specific pathology of adnexal masses
Sayasneh, A., Kaijser, J., Preisler, J., Smith, A. A., Raslan, F., Johnson, S., Husicka, R., Ferrara, L., Stalder, C. M., Ghaem‐Maghami, S., et al. (2014) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.14675 · Cited by 40
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) · Diagnostic and Prognostic Research · doi:10.1186/s41512-021-00096-5 · Cited by 39
Staging by imaging in gynecologic cancer and the role of ultrasound: an update of European joint consensus statements
Fischerová, D., Smet, C., Scovazzi, U., Sousa, D. N., Hundarova, K., & Haldorsen, I. S. (2024) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2023-004609 · Cited by 41
A systematic approach to adnexal masses discovered on ultrasound: the ADNEx MR scoring system
Sadowski, E. A., Robbins, J. B., Rockall, A., & Thomassin‐Naggara, I. (2017) · Abdominal Radiology · doi:10.1007/s00261-017-1272-7 · Cited by 39
Survival of Women With Type I and II Epithelial Ovarian Cancer Detected by Ultrasound Screening
van Nagell, J. R., Burgess, B. T., Miller, R. W., Baldwin, L. A., DeSimone, C. P., Ueland, F. R., Huang, B., Chen, Q., Kryscio, R. J., & Pavlik, E. J. (2018) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000002921 · Cited by 39
Ultrasound in Gynecological Cancer: Is It Time for Re-evaluation of Its Uses?
Fischerová, D., & Cibula, D. (2015) · Current Oncology Reports · doi:10.1007/s11912-015-0449-x · Cited by 39
The ADNEX model to triage adnexal masses: An external validation study and comparison with the IOTA two-step strategy and subjective assessment by an experienced ultrasound operator
Viora, E., Piovano, E., Poma, C. B., Cotrino, I., Castiglione, A., Cavallero, C., Sciarrone, A., Bastonero, S., Iskra, L., & Zola, P. (2020) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2020.02.022 · Cited by 38
Scoring systems for the evaluation of adnexal masses nature: current knowledge and clinical applications
Terzić, M., Aimagambetova, G. N., Norton, M., Corte, L. D., Marín-Buck, A., Lisón, J. F., Amer-Cuenca, J. J., Zito, G., Garzon, S., Caruso, S., et al. (2020) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2020.1732892 · Cited by 39
“Therefore, the IOTA ADNEX model can help to differentiate benign from malignant tumours, as well as identify the histologic types and tumour spread (Van Calster et al. 2014).”
BackgroundMethod
Diagnostic accuracy and inter-observer reliability of the O-RADS scoring system among staff radiologists in a North American academic clinical setting
Pi, Y., Wilson, M. P., Katlariwala, P., Sam, M., Ackerman, T. E., Paskar, L., Patel, V., & Low, G. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-03193-7 · Cited by 38
Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review
Moro, F., Giudice, M. T., Ciancia, M., Zaçe, D., Baldassari, G., Vagni, M., Tran, H. E., Scambia, G., & Testa, A. C. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29171 · Cited by 45
Validation of IOTA-ADNEX Model in Discriminating Characteristics of Adnexal Masses: A Comparison with Subjective Assessment
Jeong, S. Y., Park, B. K., Lee, Y.-Y., & Kim, T. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9062010 · Cited by 37
“In 2014, a new model with better performance, the assessment of different neoplasias in the adnexa (ADNEX) model, was developed in this group [13].”
Method
A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125
Chiappa, V., Interlenghi, M., Bogani, G., Salvatore, C., Bertolina, F., Sarpietro, G., Signorelli, M., Ronzulli, D., Castiglioni, I., & Raspagliesi, F. (2021) · European Radiology Experimental · doi:10.1186/s41747-021-00226-0 · Cited by 35
Role of CA125/CEA ratio and ultrasound parameters in identifying metastases to the ovaries in patients with multilocular and multilocular‐solid ovarian masses
Moro, F., Pasciuto, T., Djokovic, D., Legge, A. D., Granato, V., Moruzzi, M. C., Mancari, R., Zannoni, G. F., Fischerová, D., Franchi, D., et al. (2018) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.19174 · Cited by 33
Low-grade epithelial ovarian cancer: what a radiologist should know
Elsherif, S. B., Javadi, S., Viswanathan, C., Faria, S. C., & Bhosale, P. (2019) · British Journal of Radiology · doi:10.1259/bjr.20180571 · Cited by 32
Management of ovarian masses in pregnancy: patient selection for interventional treatment
Testa, A. C., Mascilini, F., Quagliozzi, L., Moro, F., Bolomini, G., Mirandola, M., Moruzzi, M. C., Scambia, G., & Fagotti, A. (2020) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2020-001996 · Cited by 32
Comparison of O‐RADS , GI‐RADS, and ADNEX for Diagnosis of Adnexal Masses: An External Validation Study Conducted by Junior Sonologists
Lai, H.-W., Lyu, G., Kang, Z., Li, L., Zhang, Y., & Huang, Y. (2021) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15834 · Cited by 32
Comparison of the O-RADS and ADNEX models regarding malignancy rate and validity in evaluating adnexal lesions
Chen, G.-Y., Hsu, T., Chan, I., Liu, C., Chao, W., Shih, Y.-C., Jiang, L., Chang, Y., Wang, P., & Chen, Y. (2022) · European Radiology · doi:10.1007/s00330-022-08803-6 · Cited by 34
The use of sonographic subjective tumor assessment, IOTA logistic regression model 1, IOTA Simple Rules and GI-RADS system in the preoperative prediction of malignancy in women with adnexal masses
Koneczny, J., Czekierdowski, A., Florczak, M., Poziemski, P., Stachowicz, N., & Borowski, D. (2017) · Ginekologia Polska · doi:10.5603/gp.a2017.0116 · Cited by 32
Ovarian masses with papillary projections diagnosed and removed during pregnancy: ultrasound features and histological diagnosis
Mascilini, F., Savelli, L., Scifo, M. C., Exacoustós, C., Timor‐Tritsch, I. E., Blasis, I. D., Moruzzi, M. C., Pasciuto, T., Scambia, G., Valentin, L., et al. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17216 · Cited by 31
ISUOG Consensus Statement on rationalization of gynecological ultrasound services in context of SARS-CoV-2
Bourne, T., Leonardi, M., Kyriacou, C., Al‐Memar, M., Landolfo, C., Cibula, D., Condous, G., Metzger, U., Fischerova, D., Timmerman, D., et al. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.22047 · Cited by 29
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) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01234-y · Cited by 33
Differences in ultrasound features of papillations in unilocular‐solid adnexal cysts: a retrospective international multicenter study
Landolfo, C., Valentin, L., Franchi, D., Holsbeke, C. V., Fruscio, R., Froyman, W., Sladkevicius, P., Kaijser, J., Ameye, L., Bourne, T., et al. (2018) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.18951 · Cited by 30
Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study
Du, Y., Guo, W., Xiao, Y., Chen, H., Yao, J., & Wu, J. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01251-2 · Cited by 34
“[12, 29, 30].”
Variation in the initial assessment and investigation for ovarian cancer in symptomatic women: a systematic review of international guidelines
Funston, G., van Melle, M., Baun, M. L., Jensen, H., Helsper, C. W., Emery, J. D., Crosbie, E. J., Thompson, M. J., Hamilton, W. T., & Walter, F. M. (2019) · BMC Cancer · doi:10.1186/s12885-019-6211-2 · Cited by 29
MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors
Yu, X., Wang, L., Yu, H., Zou, Y., Wang, C., Jiao, J., Hong, H., & Zhang, S. (2021) · Cancer Management and Research · doi:10.2147/cmar.s284220 · Cited by 28
“models have been proposed to distinguish benign, borderline, and malignant ovarian tumors.(19,20) The MDCT diagnosis of ovarian malignant tumors is related to various findings, such as the presence of bilateral and cystic-solid masses, a large soft-tissue component with necrosis, a tumor with thick and irregular septa…”
Background
Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort
Yoeli‐Bik, R., Longman, R. E., Wroblewski, K., Weigert, M., Abramowicz, J. S., & Lengyel, E. R. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.23289 · Cited by 29
Towards an evidence-based approach for diagnosis and management of adnexal masses: findings of the International Ovarian Tumour Analysis (IOTA) studies.
Kaijser, J. (2015) · PubMed Central · Cited by 27

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