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

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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

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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

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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.

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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

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Refining Ovarian Cancer Test accuracy Scores (ROCkeTS): protocol for a prospective longitudinal test accuracy study to validate new risk scores in women with symptoms of suspected ovarian cancer
Sundar, S. S., Rick, C., Dowling, F., Au, P., Snell, K. I. E., Rai, N., Champaneria, R., Stobart, H., Neal, R. D., Davenport, C. F., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010333 · Cited by 27
Nucleosome footprinting in plasma cell-free DNA for the pre-surgical diagnosis of ovarian cancer
Vanderstichele, A., Busschaert, P., Landolfo, C., Olbrecht, S., Coosemans, A., Froyman, W., Loverix, L., Concin, N., Braicu, E. I., Wimberger, P., et al. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00300-5 · Cited by 27
Comparison of the Diagnostic Performances of Ultrasound-Based Models for Predicting Malignancy in Patients With Adnexal Masses
Qian, L., Du, Q., Jiang, M., Yuan, F., Chen, H., & Feng, W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.673722 · Cited by 24
The Ovarian/Adnexal Reporting and Data System for Ultrasound: From Standardized Terminology to Optimal Risk Assessment and Management
Phillips, C. H., Guo, Y., Strachowski, L. M., Jha, P., Reinhold, C., & Andreotti, R. F. (2022) · Canadian Association of Radiologists Journal · doi:10.1177/08465371221108057 · Cited by 25
Recommendations of the Polish Gynecological Oncology Society for the diagnosis and treatment of ovarian cancer
Basta, A., Bidziński, M., Bieńkiewicz, A., Blecharz, P., Bodnar, L., Jach, R., Knapp, P., Kojs, Z., Kotarski, J., Markowska, J., et al. (2017) · Ginekologia Onkologiczna/Current Gynecologic Oncology · doi:10.15557/cgo.2017.0001 · Cited by 25
Ovarian Strumal Carcinoid: Case Report, Systematic Literature Review and Pooled Analysis
Turla, A., Zamparini, M., Milione, M., Grisanti, S., Amoroso, V., Pedersini, R., Cosentini, D., & Berruti, A. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.871210 · Cited by 24
Myeloid-derived suppressor cells at diagnosis may discriminate between benign and malignant ovarian tumors
Coosemans, A., Baert, T., Ceusters, J., Busschaert, P., Landolfo, C., Verschuere, T., Rompuy, A. V., Vanderstichele, A., Froyman, W., Neven, P., et al. (2019) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2019-000521 · Cited by 24
A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses
Guo, Y., Jiang, T., Ouyang, L., Li, X., He, W., Zhang, Z., Shen, H., You, Z., Yang, G., & Lai, H. (2020) · Gynecologic Oncology · doi:10.1016/j.ygyno.2020.12.006 · Cited by 23
Safety and efficiency of performing transvaginal ultrasound-guided tru-cut biopsy for pelvic masses
Verschuere, H., Froyman, W., den Bosch, T. V., Hoefs, M. V., Kaijser, J., Schoubroeck, D. V., Rompuy, A. V., Vergote, I. B., & Timmerman, D. (2021) · Gynecologic Oncology · doi:10.1016/j.ygyno.2021.03.026 · Cited by 23
Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review
Koch, A. H., Jeelof, L. S., Muntinga, C. L. P., Gootzen, T. A., van de Kruis, N. M. A., Nederend, J., Boers, T. G. W., van der Sommen, F., & Piek, J. M. J. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01345-x · Cited by 25
“…have been developed by the International Ovarian tumor analysis (IOTA) group, such as the Assessment of Different NEo-plasias in the adneXa (ADNEX) model, which combines six ultrasound features together with patients age, serum CA125 and type of center (oncology referral center vs other) [3, 4].”
Method
Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound
Barcroft, J. F., Linton‐Reid, K., Landolfo, C., Al‐Memar, M., Parker, N., Kyriacou, C., Munaretto, M., Fantauzzi, M., Cooper, N., Yazbek, J., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00527-8 · Cited by 26
“ADNEX (with CA-125) is the best-performing ultrasound-based model with an AUC of 0.94, sensitivity of 0.865 ( ﬁ xed speci ﬁ city 0.90) and speci ﬁ city of 0.866 ( ﬁ xed sensitivity 0.90) 9,11 .”
BackgroundMethodInfluential
Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound
Moro, F., Vagni, M., Tran, H. E., Bernardini, F., Mascilini, F., Ciccarone, F., Nero, C., Giannarelli, D., Boldrini, L., Fagotti, A., et al. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27680 · Cited by 25
How to develop, validate, and update clinical prediction models using multinomial logistic regression
Gehringer, C. K., Martin, G. P., CALSTER, B. V., Hyrich, K. L., Verstappen, S. M. M., & Sergeant, J. C. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111481 · Cited by 22
“MPMs have been applied to several clinical settings(2,18–21), however, the adoption of this approach is still relatively scarce(13).”
MethodInfluential
Reliability of IOTA score and ADNEX model in the screening of ovarian malignancy in postmenopausal women
Nohuz, E., Simone, L. D., & Chene, G. (2018) · Journal of Gynecology Obstetrics and Human Reproduction · doi:10.1016/j.jogoh.2018.04.012 · Cited by 23
Factors associated with misdiagnosis of frozen section of mucinous borderline ovarian tumor
Zhang, W., Jia, S., Xiang, Y., Yang, J., Jia, C., & Leng, J. (2018) · Journal of International Medical Research · doi:10.1177/0300060518795582 · Cited by 22
Guideline No. 404: Initial Investigation and Management of Benign Ovarian Masses
Wolfman, W., Thurston, J., Yeung, G., & Glanc, P. (2020) · Journal of Obstetrics and Gynaecology Canada · doi:10.1016/j.jogc.2020.01.014 · Cited by 23
Structured report for dynamic ultrasonography in patients with suspected or known endometriosis: Recommendations of the International Society for Gynecologic Endoscopy (ISGE)
Djokovic, D., Pinto, P., van Herendael, B. J., Laganà, A. S., Thomas, V., & Keckstein, J. (2021) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2021.06.035 · Cited by 22
Estimating the risk of malignancy of adnexal masses: validation of the ADNEX model in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China
He, P., Wang, J., Duan, W., Song, C., Yu, Y., & Wu, Q. (2021) · Journal of Ovarian Research · doi:10.1186/s13048-021-00922-w · Cited by 21
“The Assessment of Different NEoplasias in the adneXa (ADNEX) model is the first predictive multiclass model developed by the IOTA and is able to differentiate between benign tumours, borderline ovarian tumours (BOTs), stage I ovarian cancer (OC), stage II-IV OC and secondary metastatic ovarian cancers [12].”
BackgroundResultInfluential
Diagnostic Performance of Risk of Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI) and Expert Ultrasound Assessment in a Pelvic Mass Classified as Inconclusive by International Ovarian Tumour Analysis (IOTA) Simple Rules
Ngu, S., Chai, Y. K., Choi, K. M., Leung, T. W., Li, J., Kwok, S. T., Chu, M. M. Y., Tse, K. Y., Cheung, V. Y. T., Ngan, H., et al. (2022) · Cancers · doi:10.3390/cancers14030810 · Cited by 22
Radiomics and Radiogenomics of Ovarian Cancer
Panico, C., Avesani, G., Zormpas‐Petridis, K., Rundo, L., Nero, C., & Sala, E. (2023) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2023.02.006 · Cited by 24
Preoperative Evaluation of the ADNEX Model for the Prediction of the Ovarian Cancer Risk of Adnexal Masses at Siriraj Hospital
Poonyakanok, V., Tanmahasamut, P., Jaishuen, A., Wongwananuruk, T., Asumpinwong, C., Panichyawat, N., & Chantrapanichkul, P. (2021) · Gynecologic and Obstetric Investigation · doi:10.1159/000513517 · Cited by 21
Diagnostic Models Combining Clinical Information, Ultrasound and Biochemical Markers for Ovarian Cancer: Cochrane Systematic Review and Meta-Analysis
Davenport, C. F., Rai, N., Sharma, P., Deeks, J. J., Berhane, S., Mallett, S., Saha, P., Solanki, R., Bayliss, S. E., Snell, K. I. E., et al. (2022) · Cancers · doi:10.3390/cancers14153621 · Cited by 22
“Three studies were found, two from single centres [45,46] and one multicentre study [47].”
Background
A Deep Learning Model System for Diagnosis and Management of Adnexal Masses
Li, J., Chen, Y., Zhang, M., Zhang, P., He, K., Yan, F., Li, J., Xu, H. L., Burkhoff, D., Luo, Y., et al. (2022) · Cancers · doi:10.3390/cancers14215291 · Cited by 21
“If more than one mass was detected, then the mass with the most complex morphology or, in the case of a similar morphology, the largest diameter, was used [16,17].”
Method
Sonographic Assessment of Complex Ultrasound Morphology Adnexal Tumors in Pregnant Women with the Use of IOTA Simple Rules Risk and ADNEX Scoring Systems
Czekierdowski, A., Stachowicz, N., Smoleń, A., Kluz, T., Łoziński, T., Miturski, A., & Kraczkowski, J. J. (2021) · Diagnostics · doi:10.3390/diagnostics11030414 · Cited by 20
“According to the results of the IOTA group’s recent studies, a cut-off risk set at 20% is related to the best balance between ADNEX model sensitivity and specificity [17].”
MethodResult
Evaluation of the Diagnostic Value of the Ultrasound ADNEX Model for Benign and Malignant Ovarian Tumors
Peng, X., Ma, Y., Wang, L., Li, H., Zheng, X., & Liu, Y. (2021) · International Journal of General Medicine · doi:10.2147/ijgm.s328010 · Cited by 20
Solid Hypoechoic Adnexal Lesions with Acoustic Shadowing Warrant an MRI Recommendation in the O-RADS Risk Stratification and Management System
Wilson, M. P., Katlariwala, P., & Low, G. (2020) · Radiology · doi:10.1148/radiol.2020200437 · Cited by 20
Limitations of three-dimensional power Doppler angiography in preoperative evaluation of ovarian tumors
Silvestre, L., Martins, W. P., & dos Reis, F. J. C. (2015) · Journal of Ovarian Research · doi:10.1186/s13048-015-0174-y · Cited by 19
“Among our cases, 43 % were malignant, this prevalence is similar to the reported prevalence of 42 % of malignancy in oncology centers and 17 % in other centers [27].”
Result
Comparison of Ultrasound Scores in Differentiating between Benign and Malignant Adnexal Masses
Pelayo, M., Pelayo‐Delgado, I., Saúco, J. S., Sánchez-Zurdo, J., Abarca-Martínez, L., Corraliza-Galán, V., Martín-Gromaz, C., Pablos-Antona, M. J., Zurita-Calvo, J., & Alcázar, J. L. (2023) · Diagnostics · doi:10.3390/diagnostics13071307 · Cited by 20
Ultrasound evaluation of ovarian masses and assessment of the extension of ovarian malignancy
Moro, F., Esposito, R., Landolfo, C., Froyman, W., Timmerman, D., Bourne, T., Scambia, G., Valentin, L., & Testa, A. C. (2021) · British Journal of Radiology · doi:10.1259/bjr.20201375 · Cited by 20
Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen
Seebacher, V., Aust, S., D’Andrea, D., Grimm, C., Reiser, E., Tiringer, D., von Mersi, H., Polterauer, S., Reinthaller, A., & Helmy-Bader, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182383 · Cited by 21
Assessment of protein biomarkers for preoperative differential diagnosis between benign and malignant ovarian tumors
Landolfo, C., Achten, E., Ceusters, J., Baert, T., Froyman, W., Heremans, R., Vanderstichele, A., Thirion, G., Hoylandt, A. V., Claes, S., et al. (2020) · Gynecologic Oncology · doi:10.1016/j.ygyno.2020.09.025 · Cited by 18
Diagnostic Accuracy of the ADNEX Model for Ovarian Cancer at the 15% Cut-Off Value: A Systematic Review and Meta-Analysis
Huang, X., Wang, Z., Zhang, M., & Luo, H. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.684257 · Cited by 17
Adnexal Masses in Pregnancy
de Oca, M. K. M., Dotters-Katz, S. K., Kuller, J. A., & Previs, R. A. (2021) · Obstetrical & Gynecological Survey · doi:10.1097/ogx.0000000000000909 · Cited by 20
“Symptoms of ovarian cancer are vague and can mimic other conditions such as pregnancy or gastrointestinal-related disorders.(68,71) Several studies have investigated the ability of ultrasound to correctly diagnose an adnexal mass in pregnancy.”
Background
Transvaginal Sonographic Imaging and Associated Techniques for Diagnosis of Ovarian, Deep Endometriosis, and Adenomyosis: A Comprehensive Review
Aas‐Eng, M. K., Montanari, E., Lieng, M., Keckstein, J., & Hudelist, G. (2020) · Seminars in Reproductive Medicine · doi:10.1055/s-0040-1718740 · Cited by 18
Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study
Dai, W., Wu, Y., Ling, Y.-T., Zhao, J., Zhang, S., Gu, Z.-W., Gong, L., Zhu, M.-N., Dong, S., Xu, S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102923 · Cited by 18
Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS
Liu, L., Cai, W., Tian, H., Wu, B., Zhang, J., Wang, T., Hao, Y., & Yue, G. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1377489 · Cited by 18
Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images
Giourga, M., Petropoulos, I., Stavros, S., Potiris, A., Gerede, A., Sapantzoglou, I., Fanaki, M., Papamattheou, E., Karasmani, C., Karampitsakos, T., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13144123 · Cited by 19
Clinical Utility and Diagnostic Accuracy of ROMA, RMI, ADNEX, HE4, and CA125 in the Prediction of Malignancy in Adnexal Masses
Spagnol, G., Marchetti, M., Carollo, M., Bigardi, S., Tripepi, M., Facchetti, E., Tommasi, O. D., Vitagliano, A., Cavallin, F., Tozzi, R., et al. (2024) · Cancers · doi:10.3390/cancers16223790 · Cited by 19
Microcystic pattern and shadowing are independent predictors of ovarian borderline tumors and cystadenofibromas in ultrasound
Zheng, X., Lyu, G., Gan, Y., Hu, M., Liu, X., Chen, S., Wu, X., Yang, W., Ye, F., Yan, X.-B., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07113-z · Cited by 17
Ovarian sex cord stromal tumours: analysis of the clinical and sonographic characteristics of different histopathologic subtypes
Jiang, M., Le, Q., Yang, B., Yuan, F., & Chen, H. (2021) · Journal of Ovarian Research · doi:10.1186/s13048-021-00805-0 · Cited by 17
“The IOTA ADNEX model [10] contains nine predictors (three clinical and six ultrasound variables): age, serum CA125 level, type of centre (oncology referral centre vs non-oncology centre), maximum diameter of the lesion, maximal diameter of the largest solid part of the lesion, more than 10 cyst locules (yes or no), nu…”
Method
ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumours
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) · Facts Views and Vision in ObGyn · doi:10.52054/fvvo.13.2.016 · Cited by 17
Predictive Analytics: The Application of J48 Algorithm on Grocery Data to Predict Obesity
Daud, N., Noor, N. L. M., Aljunid, S. A., Noordin, N., & Teng, N. I. M. F. (2018) · IEEE Conference on Big Data and Analytics (ICBDA) · doi:10.1109/icbdaa.2018.8629623 · Cited by 17
“Calibration refers to the assessment of how well a model could predict, in comparison to the actual events [24].”
Background
Development and validation of a model that includes two ultrasound parameters and the plasma D-dimer level for predicting malignancy in adnexal masses: an observational study
Stukan, M., Badocha, M., & Ratajczak, K. (2019) · BMC Cancer · doi:10.1186/s12885-019-5629-x · Cited by 17
Basic Gynecologic Ultrasound Examination (Level I): DEGUM, ÖGUM, and SGUM Recommendations
Grab, D., Merz, E., Eichhorn, K., Tutschek, B., Kagan, K. O., Heling, K., Faber, R., Klaritsch, P., Rempen, A., Kähler, C., et al. (2022) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/a-1851-5157 · Cited by 19
Simple rules, O-RADS, ADNEX and SRR model: Single oncologic center validation of diagnostic predictive models alone and combined (two-step strategy) to estimate the risk of malignancy in adnexal masses and ovarian tumors
Spagnol, G., Marchetti, M., Tommasi, O. D., Vitagliano, A., Cavallin, F., Tozzi, R., Saccardi, C., & Noventa, M. (2023) · Gynecologic Oncology · doi:10.1016/j.ygyno.2023.08.012 · Cited by 16
The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play
Weichert, J., Welp, A., Scharf, J. L., Dracopoulos, C., Becker, W.-H., & Gembicki, M. (2021) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1522-3029 · Cited by 19
Prédictibilité préopératoire de la malignité des tumeurs ovariennes à partir du score ADNEX et utilisation en pratique clinique
Joyeux, E., Miras, T., Masquin, I., Duglet, P.-E., Astruc, K., & Douvier, S. (2016) · Gynécologie Obstétrique & Fertilité · doi:10.1016/j.gyobfe.2016.07.007 · Cited by 16
Cytokines in peritoneal fluid of ovarian neoplasms
de Lima, C. A., Rodrigues, I. S. S., Martins-Filho, A., Micheli, D. C., Tavares‐Murta, B. M., Murta, E. F. C., & Nomelini, R. S. (2019) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2019.1633516 · Cited by 16
“ADNEX discriminates between benign and malignant tumours, offering a more refined approach to treatment for women with some adnexal mass, and for the diagnosis and treatment of patients with ovarian neoplasia (Van Calster et al. 2014; Froyman et al. 2017).”
Background
O-RADS Classification for Ultrasound Assessment of Adnexal Masses: Agreement between IOTA Lexicon and ADNEX Model for Assigning Risk Group
Vara, J., Pagliuca, M., Springer, S., Canales, J. G., Brotóns, I., Yakcich, J., AJOSSA, S., Pascual, M. A., Guerriero, S., & Alcázar, J. L. (2023) · Diagnostics · doi:10.3390/diagnostics13040673 · Cited by 17
A human-interpretable machine learning pipeline based on ultrasound to support leiomyosarcoma diagnosis
Lombardi, A., Arezzo, F., Sciascio, E. D., Ardito, C. A., Mongelli, M., Lillo, N. D., Fascilla, F. D., Silvestris, E., Kardhashi, A., Putino, C., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102697 · Cited by 15
Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study
He, X., Bai, X.-H., Chen, H., & Feng, W. (2024) · Journal of Ovarian Research · doi:10.1186/s13048-024-01544-8 · Cited by 16
A systematic review and meta-analysis of the use of ultrasound to diagnose borderline ovarian tumours
Otify, M., Laios, A., Shamy, T. E., D’Angelo, A., & Amso, N. N. (2019) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2019.11.016 · Cited by 15
Ultrasound Assessment of Adnexal Pathology: Standardized Methods and Different Levels of Experience
Tavoraitė, I., Kronlachner, L., Opolskiene, G., & Bartkevičienė, D. (2021) · Medicina · doi:10.3390/medicina57070708 · Cited by 14
Evaluating use of two‐step International Ovarian Tumor Analysis strategy to classify adnexal masses identified in pregnancy: pilot study
Barcroft, J. F., Pandrich, M., Forno, S. D., Cooper, N., Linton‐Reid, K., Landolfo, C., Timmerman, D., Saso, S., & Bourne, T. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27707 · Cited by 14
Value of Assessment of Different Neoplasias in the Adnexa in the Differential Diagnosis of Malignant Ovarian Tumor and Benign Ovarian Tumor: A Meta-analysis
Yue, X., Zhong, L., Wang, Y., Zhang, C., Chen, X., Wang, S., Hu, J., Hu, J., Wang, C., & Liu, X. (2022) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2022.02.001 · Cited by 15
Ultrasound Features and Ultrasound Scores in the Differentiation between Benign and Malignant Adnexal Masses
Pelayo, M., Saúco, J. S., Sánchez-Zurdo, J., Abarca-Martínez, L., Borrero, C., Sainz‐Bueno, J. A., Alcázar, J. L., & Pelayo‐Delgado, I. (2023) · Diagnostics · doi:10.3390/diagnostics13132152 · Cited by 17
“The ADNEX model (Assessment of Different Neoplasia in the Adnexa) was published by the IOTA group as another complementary score that calculate the probability of malignancy [7].”
Method
Development of a fuzzy-driven system for ovarian tumor diagnosis
Żywica, P., Dyczkowski, K., Wójtowicz, A., Stachowiak, A., Szubert, S., & Moszyński, R. (2016) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2016.08.003 · Cited by 14
“Among these, the most important are the LR1 and LR2 models, based on logistic regression [6], and the most recent IOTA model – ADNEX [7].”
Role of ultrasound in advanced peritoneal malignancies
Blasis, I. D., Moruzzi, M. C., Moro, F., Mascilini, F., Cianci, S., Alletti, S. G., Turco, L. C., Garganese, G., Scambia, G., & Testa, A. C. (2019) · Minerva Medica · doi:10.23736/s0026-4806.19.06103-2 · Cited by 14
Validation of ADNEX and IOTA two‐step strategy and estimation of risk of complications during follow‐up of adnexal masses in low‐risk population
Pascual, M. A., Vancraeynest, L., Timmerman, S., Ceusters, J., Ledger, A., Graupera, B., Rodríguez, I., Valero, B., Landolfo, C., Testa, A. C., et al. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27642 · Cited by 16
Past, Present and Future Ultrasonographic Techniques for Analyzing Ovarian Masses
Guerriero, S., Saba, L., Alcázar, J. L., Pascual, M. A., AJOSSA, S., Perniciano, M., Piras, A., Sedda, F., Peddes, C., Fabbri, P., et al. (2015) · Women s Health · doi:10.2217/whe.15.11 · Cited by 13
Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer
Yang, S., Tang, J. Y., Rong, Y., Wang, M., Long, J., Chen, C., & Wang, C. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.949766 · Cited by 12
The physiological sonographic features of the ovary in healthy subjects: a joint systematic review and meta-analysis by the Italian Society of Gynecology and Obstetrics (SIGO) and the Italian Society of Endocrinology (SIE)
Moro, F., Scavello, I., Maseroli, E., Rastrelli, G., Poma, C. B., Bonin, C., Dassie, F., Federici, S., Fiengo, S., Guccione, L., et al. (2022) · Journal of Endocrinological Investigation · doi:10.1007/s40618-022-01939-8 · Cited by 13
Etiology of Ascites and Pleural Effusion Associated with Ovarian Tumors: Literature Review and Case Reports of Three Ovarian Tumors Presenting with Massive Ascites, but without Peritoneal Dissemination
Miyoshi, A., Miyatake, T., Hara, T., Tanaka, A., Komura, N., Komiya, S., Kanao, S., Takeda, M., Mimura, M., Nagamatsu, M., et al. (2015) · Case Reports in Obstetrics and Gynecology · doi:10.1155/2015/414019 · Cited by 13
“However, the size of borderline ovarian tumors is relatively large, so they are often suspected of being ovarian cancers, having a much direr prognosis [3].”
Background
Performance of the IOTA ADNEX Model on Selected Group of Patients with Borderline Ovarian Tumours
Gaurilčikas, A., Gedgaudaitė, M., Čižauskas, A., Atstupėnaitė, V., Paškauskas, S., Gaurilcikiene, D., Biržietis, T., Vaitkienė, D., & Nadišauskienė, R. J. (2020) · Medicina · doi:10.3390/medicina56120690 · Cited by 13
“For the calculation of risk, the ADNEX model uses three clinical variables (patients’ age, Ca125 tumour marker, and whether the ultrasound scan was performed in an oncology center or not) and six ultrasound variables (maximal diameter of the lesion, maximal diameter of the largest solid part, presence of more than 10…”
MethodInfluential
Ultrasonographic evaluation of ovarian mass for predicting malignancy in pregnant women
Lee, S. J., Kim, Y. H., Lee, M., Ko, H. S., Oh, S., Seol, H., Kim, J. W., Ahn, K. H., Na, S., Seong, W. J., et al. (2021) · Gynecologic Oncology · doi:10.1016/j.ygyno.2021.09.007 · Cited by 13
Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery
Landolfo, C., Ceusters, J., Valentin, L., Froyman, W., Van Gorp, T., Heremans, R., Baert, T., Wouters, R., Vankerckhoven, A., Van Rompuy, A.-S., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02578-x · Cited by 14
“The Assessment of Different NEoplasias in the adneXa (ADNEX) is a multinomial logistic regression model developed by the International Ovarian Tumour Analysis (IOTA) group [12].”
Comparison of ultrasound−based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study
Hu, Y., Chen, B., Dong, H., Sheng, B., Xiao, Z., Li, J., Tian, W., & Lv, F. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1101297 · Cited by 12
Ultrasound Features and Clinical Outcome of Patients with Ovarian Masses Diagnosed during Pregnancy: Experience of Single Gynecological Ultrasound Center
Bruno, M., Capanna, G., Stanislao, V., Ciuffreda, R., Tabacco, S., Fantasia, I., Florio, C. D., Stabile, G., D’Alfonso, A., Guido, M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13203247 · Cited by 12
“These methods include the evaluation of different NEOplasias in the adneXa (ADNEX) model [20] introduced in 2014 and the simple rule risk (SRR) model proposed in 2016 [21].”
Method
Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
Ledger, A., Ceusters, J., Valentin, L., Testa, A. C., Holsbeke, C. V., Franchi, D., Bourne, T., Froyman, W., Timmerman, D., & CALSTER, B. V. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02103-3 · Cited by 12
Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two‐step strategy using benign descriptors and ADNEX model: Portuguese multicenter study
Borges, A. L., Brito, M., Ambrósio, P., Condeço, R., Pinto, P., Ambrósio, B., Mahomed, F., Gama, J. M. R., Bernardo, M. J., Gouveia, A., et al. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27641 · Cited by 12
Economic evaluation of an expert examiner and different ultrasound models in the diagnosis of ovarian cancer
Meys, E. M. J., Jeelof, L. S., Ramaekers, B. L. T., Dirksen, C. D., Kooreman, L. F. S., Slangen, B. F. M., Kruitwagen, R. F. P. M., & Gorp, T. V. (2018) · European Journal of Cancer · doi:10.1016/j.ejca.2018.05.003 · Cited by 12
Ultrasound features and clinical outcome of patients with malignant ovarian masses diagnosed during pregnancy: experience of a gynecological oncology ultrasound center
Moro, F., Mascilini, F., Pasciuto, T., Leombroni, M., Destri, M. L., Blasis, I. D., Garofalo, S., Scambia, G., & Testa, A. C. (2019) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2019-000373 · Cited by 12
“Ultrasound examination is also an effective tool in distinguishing a primary ovarian cancer from metastasis.(8) 9 However, adnexal masses may be difficult to classify during pregnancy as hormonal changes associated with the pregnancy may cause alterations in the sonographic appearances of ovarian masses and, for insta…”
Method
Colon carcinoma presenting as ovarian metastasis
Kemps, P. G., Bol, M., Steller, E. J. A., de Pont, L. M. H., Holterhues, C., van Gerven, L., & Kolkman, W. (2021) · Radiology Case Reports · doi:10.1016/j.radcr.2021.06.072 · Cited by 12
Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors—A Comparative Study with Review of the Literature
Horała, A., Plewa, S., Dereziński, P., Klupczyńska, A., Matysiak, J., Nowak‐Markwitz, E., & Kokot, Z. J. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18042167 · Cited by 12
Two‐Step Strategy for Optimizing the Preoperative Classification of Adnexal Masses in a University Hospital, Using International Ovarian Tumor Analysis Models
Esquivel, A. L., Rodríguez, N., Ayala, N., Buriticá, C., Gómez, A., Velandia, A., Rodríguez, N., & Alcázar, J. L. (2021) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15728 · Cited by 12
“Within these models, the IOTA Simple Rules and the IOTA ADNEX model are the best predictive tests validated externally, because both predict ovarian cancer in patients with persistent adnexal masses.(2,7) The IOTA Simple Rules is based on five ultrasound features suggestive of benignity and five ultrasound features su…”
Method
AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging
Whitney, H. M., Yoeli‐Bik, R., Abramowicz, J. S., Li, L., Li, H., Longman, R. E., Lengyel, E. R., & Giger, M. L. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.4.044505 · Cited by 11
“…such as the International Ovarian Tumor Analysis (IOTA) Simple Rules, 12 the IOTA Assessment of Different NEoplasias in the adneXa model, 13 and the American College of Radiology Ovarian-Adnexal Reporting and Data (O-RADS) 14 risk stratification system with the goal of reducing false…”
Assessment of Risk Factors Associated with Severe Endometriosis and Establishment of Preoperative Prediction Model
Yang, Y., Li, J., Chen, H., & Feng, W. (2022) · Diagnostics · doi:10.3390/diagnostics12102348 · Cited by 11
“Sonographic Parameters According to the judging principle [15], 258 left-sided and 232 right-sided ovary cysts were analyzed.”
MethodInfluential
The International Ovarian Tumor Analysis-Assessment of Different Neoplasias in the Adnexa (IOTA-ADNEX) Model Assessment for Risk of Ovarian Malignancy in Adnexal Masses
Cherukuri, S., Jajoo, S., & Dewani, D. (2022) · Cureus · doi:10.7759/cureus.31194 · Cited by 11
Low-grade mucinous appendiceal neoplasm mimicking an ovarian lesion: A case report and review of literature
Borges, A. L., Reis‐de‐Carvalho, C., Chorão, M., Pereira, H., & Djokovic, D. (2021) · World Journal of Clinical Cases · doi:10.12998/wjcc.v9.i10.2334 · Cited by 11
External validation of ADNEX model for diagnosing ovarian cancer: evaluating performance of differentiation between tumor subgroups
CALSTER, B. V. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17391 · Cited by 11
Prediction Models of Adnexal Masses: State-of-the-Art Review
Mina, M., Kosmas, I. P., Tsakiridis, I. G., Mamopoulos, A. M., Kalogiannidis, I. A., Athanasiadis, A. P., & Dagklis, T. (2021) · Obstetrical & Gynecological Survey · doi:10.1097/ogx.0000000000000873 · Cited by 10
Comparative Study of the Efficacy of the Ovarian-Adnexa Reporting and Data System Ultrasound Combined With Contrast-Enhanced Ultrasound and the ADNEX MR Scoring System in the Diagnosis of Adnexal Masses
Wang, T., Cui, W., Nie, F., Huang, X. J., Huang, L., Liu, L., Zhu, Y., & Zheng, R. (2023) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.05.012 · Cited by 10
Application of O-RADS US combined with MV-Flow to diagnose ovarian-adnexal tumors
Ruan, L., Liu, H., Xiang, H., Ni, Y., Feng, Y., Zhou, H., & Qi, M. (2023) · ULTRASONOGRAPHY · doi:10.14366/usg.23061 · Cited by 10
“In 2014, Van Calster et al. [11] developed a risk prediction model, the Assessment of Different Neoplasia in the Adnexa (ADNEX), to distinguish between benign, borderline, invasive in stage I, invasive in stages II-IV, and secondary metastatic ovarian tumors preoperatively.”
Background
Current uptake and barriers to wider use of the International Ovarian Tumor Analysis (IOTA) models in Dutch gynaecological practice
Lems, E., Leemans, J. C., Lok, C. A. R., Bongers, M. Y., & Geomini, P. M. A. J. (2023) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2023.09.018 · Cited by 10
Ovarian Cysts and Tumors in Adolescents
Stanković, Z. B. (2024) · Obstetrics and Gynecology Clinics of North America · doi:10.1016/j.ogc.2024.08.006 · Cited by 10
External validation of ultrasound-based models for differentiating between benign and malignant adnexal masses: a nationwide prospective multicenter study (IOTA phase 6)
Moro, F., Momi, M., Ledger, A., Barreñada, L., Ceusters, J., Sturla, D., Mor, E., Fornari, L., Mascilini, F., Ciccarone, F., et al. (2025) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2025.07.017 · Cited by 10
The Enemies of Reliable and Useful Clinical Prediction Models: A Review of Statistical and Scientific Challenges
CALSTER, B. V., van Smeden, M., van Amsterdam, W. A. C., Coemans, M., Wynants, L., & Steyerberg, E. W. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042324-123749 · Cited by 14
The challenging screen detection of ovarian cancer in BRCA mutation carriers adhering to a 6-month follow-up program: results from a 6-years surveillance
Grandi, G., Fiocchi, F., Cortesi, L., Toss, A., Boselli, F., Sammarini, M., Sighinolfi, G., & Facchinetti, F. (2021) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000001883 · Cited by 10
“presence of more than ten cysts [yes/no], number of papillary projections [0, 1, 2, 3,>3], presence of acoustic shadows [yes/ no], and presence of ascites [yes/no]).(12)”
BackgroundMethod
Prospective comparative trial comparing O‐RADS , IOTA ADNEX model, and RMI score for preoperative evaluation of adnexal masses for prediction of ovarian cancer
Poonyakanok, V., Tanmahasamut, P., & Jaishuen, A. (2023) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.15624 · Cited by 10
Evaluation of IOTA-ADNEX Model and Simple Rules for Identifying Adnexal Masses by Operators with Varying Levels of Expertise: A Single-Center Diagnostic Accuracy Study
Giourga, M., Pouliakis, A., Vlastarakos, P., Stavrou, S., Tsiriva, M., Gerede, A., Daskalakis, G., Voros, C., Drakakis, P., & Domali, E. (2023) · Ultrasound International Open · doi:10.1055/a-2044-2855 · Cited by 10
Tumor Growth Rate Estimates Are Independently Predictive of Therapy Response and Survival in Recurrent High-Grade Serous Ovarian Cancer Patients
Bartl, T., Karacs, J., Kreuzinger, C., Pfaffinger, S., Kendler, J., Ciocsirescu, C., Wolf, A., Reinthaller, A., Meyer, E. L., Brandstetter, M., et al. (2021) · Cancers · doi:10.3390/cancers13051076 · Cited by 10
“Numerous mathematical models have been proposed to date as potential aids in clinical decision making such as nomograms to interpret established biomarkers more comparatively or more complex risk prediction models such as the broadly validated IOTA ADNEX [18,19].”
Method
Should We Manage Large Ovarian Cysts Laparoscopically?
Paul, P. G., Chopade, G., Patil, S. R., Das, T., Thomas, M., & Garg, R. A. (2016) · Journal of Gynecologic Surgery · doi:10.1089/gyn.2016.0009 · Cited by 10
Bayesian Decision Curve Analysis With Bayesdca
Cruz, G. N. F., & Korthauer, K. D. (2024) · Statistics in Medicine · doi:10.1002/sim.10277 · Cited by 13
Diagnostic Performance of ADNEX Model and IOTA Simple Rules in Differentiating Malignant from Benign Adnexal Masses When Assessed by Non-Expert Examiners
Chankrachang, A., Lattiwongsakorn, W., Tantipalakorn, C., & Tongsong, T. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082776 · Cited by 9
Comparison of the Diagnostic Performance of Ovarian Adnexal Reporting Data System (O‐ RADS ) With IOTA Simple Rules and ADNEX Model for Classifying Adnexal Masses: A Head‐To‐Head Meta‐Analysis
Almeida, G., Bort, M. C., & Alcázar, J. L. (2025) · Journal of Clinical Ultrasound · doi:10.1002/jcu.24048 · Cited by 9
“Subsequently, in 2014, the IOTA group developed the ADNEX (Assessment of Different NEoplasias in the adneXa) model (Van Calster et al. 2014).”
The Abnormal Ovary
Hack, K., & Glanc, P. (2019) · Obstetrics and Gynecology Clinics of North America · doi:10.1016/j.ogc.2019.07.002 · Cited by 9
Accuracy of Magnetic Resonance Imaging for Identifying Ovarian Cancer in a Community-Based Setting
Lin, R., Hung, Y., Cheng, J. N., & Suh‐Burgmann, E. (2022) · Women s Health Reports · doi:10.1089/whr.2021.0106 · Cited by 9
Sensor detection in gynaecological medicine
Thompson, M. E., Ahmadi, S., Davoudian, K., Franier, B. D. L., Lotay, N., & Bernardini, M. Q. (2022) · Sensors & Diagnostics · doi:10.1039/d2sd00090c · Cited by 11
Current Update on PET/MRI in Gynecological Malignancies—A Review of the Literature
Virarkar, M., Vulasala, S. S., Calimano-Ramirez, L. F., Singh, A., Lall, C. G., & Bhosale, P. (2023) · Current Oncology · doi:10.3390/curroncol30010083 · Cited by 9
“(71%), which may lead to unnecessary surgical intervention [84,85].”
Background
Morcellation and risk of malignancy in presumed ovarian fibromas/fibrothecomas
Froyman, W., Landolfo, C., Amant, F. C., den Bosch, T. V., Vergote, I. B., Coosemans, A., Testa, A. C., Valentin, L., Bourne, T., CALSTER, B. V., et al. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)00022-x · Cited by 9

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

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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

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