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COMPARISON OF RISK OF MALIGNANCY INDICES AND ASSESSMENT OF DIFFERENT NEOPLASIAS IN THE ADNEXA (ADNEX) MODEL AS PREOPERATIVE MALİGNANCY EVALUATION METHODS.

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Presentation on theme: "COMPARISON OF RISK OF MALIGNANCY INDICES AND ASSESSMENT OF DIFFERENT NEOPLASIAS IN THE ADNEXA (ADNEX) MODEL AS PREOPERATIVE MALİGNANCY EVALUATION METHODS."— Presentation transcript:

1 COMPARISON OF RISK OF MALIGNANCY INDICES AND ASSESSMENT OF DIFFERENT NEOPLASIAS IN THE ADNEXA (ADNEX) MODEL AS PREOPERATIVE MALİGNANCY EVALUATION METHODS FOR ADNEXAL MASSES DR KEMAL SANDAL ISTANBUL MEDENIYET UNIVERSITY GOZTEPE EDUCATION AND RESEARCH HOSPITAL

2 ADNEXAL MASS Definition: Mass of the ovary, fallopian tube, or surrounding connective tissues In the United States, it is estimated that there is a 5 to 10 percent lifetime risk for women undergoing surgery for a suspected ovarian neoplasm.* *National Institutes of Health Consensus Development Conference Statement. Ovarian cancer: screening, treatment, and follow-up. Gynecol Oncol 1994; 55:S4.

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4 ADNEXAL MASS Adnexal masses may be found in females of all ages, fetuses to the elderly, and there is a wide variety of types of masses. The principal goals of the evaluation are to address acute conditions (eg, ectopic pregnancy) and to determine whether a mass is malignant.

5 PREOPERATIVE MODELS RMI (Risk of Malignancy Index)
The risk of malignancy index (RMI) was originally developed in 1990 and is a multimodality approach that combines serum CA 125, pelvic ultrasound, and menopausal status into an index score to predict the risk of ovarian cancer in women with an adnexal mass.* *Jacobs I, Oram D, Fairbanks J, et al. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br J Obstet Gynaecol 1990; 97:922.

6 RMI VERSIONS…

7 CRITERIA RMI-1 (Jacobs et al 1990) RMI-2 (Tingulstad et al 1996) RMI-3 (Tingulstad et al 1999) RMI-4(Yamamoto et al 2010) Menopausal Status (M) Premenopausal: 1 Postmenopausal: 3 Postmenopausal: 4 Ultrasound Features (U) Multiloculated Solid areas Bilaterality Ascites Metastases No Feature: 0 One Feature: 1 >1 Feature: 3 No or 1 Feature: 1 ≥2 Feature: 4 ≥2 Feature: 3 Tumor size (S) X < 7cm: 1 ≥ 7 cm: 2 Serum CA 125 Serum level of CA125 Serum level of Ca125 RMI U*M* CA125 U*M*S* CA125 Cut Off Value >200 >450

8 PREOPERATIVE MODELS ADNEX Model: The assessment of different neoplasias in the adnexa (ADNEX) model was first reported in 2014.* It is designed for use in women with an adnexal mass planned for surgery. The unique aspect of this model is that the results are intended to predict not only whether the mass is malignant, but multiple outcomes, including: benign, borderline, stage I invasive, stage II to IV invasive, and secondary metastatic adnexal tumors. *Van Calster B, Van Hoorde K, Valentin L, et al. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study. BMJ 2014; 349:g5920.

9 ADNEX MODEL (http://www.iotagroup.org/adnexmodel/site%20iota.html)

10 ADNEX MODEL

11 ADNEX MODEL  Using a previously proposed cut-off of 10%, the sensitivity for malignancy was 96.5% and specificity 71.3% on the validation data. (Timmerman et al., 2005) (Benign versus malignant) (IOTA Group Phase I dataset)

12 STUDY Retrospectively evaluated 206 patients who applied with adnexial mass to Jynecology and Obstetrics Clinics of Medeniyet University Goztepe Research Hospital between 1st of September, 2014 and 30th of July, 2016. 15 patients were excluded due to missing CA 125 and ultrasonographic measurement and recurrent ovarian cancer diagnoses.

13 STUDY 191 patient included into study
Values were calculated for RMI I-II-III-IV and ADNEX model for %5-%10-%15 malignancy risk cut off and results were compared to postoperative pathological diagnoses Benign pathology 138 case, Malignant pathology 53 case

14 RESULTS (Comparison of RMI indices in patients with malignancy prediction success in ovarian masses.) Number of patients Pathologic malignant diagnosisn (%) p Sensitivity % (%95 CI) Specificity, % (%95 CI) PPV, % (%95 CI) NPV, % (%95 CI) RMI I <0,001 66 (51,7 – 78,5) 87,7 (81 – 92,7) 67,3 (55,9-77) 87 (82,1 – 90,8) Benign 139 18 (12,9) Malignant 52 35 (67,3) RMI II 75,5 (61,7 – 86,2) 77,5 (69,7 – 84,2) 56,3 (47,7 – 64,6) 89,2 (83,6 – 93) 120 13 (10,8) 71 40 (56,3) RMI III RMI IV 67,9 (53,7 – 80,1) 89,7 (83,6 -94,3) 72 (60,2 – 81,4) 87,9 (83,1 – 91,5) 141 17 (12,1) 50 36(72)

15 RESULTS (Comparison of malignancy prediction success in ovarian masses at different cutoff values of ADNEX model in patients.) Cut-off value for malignant diagnosis Number of Patients Malignant Diagnosis p* Sensitivity,% (%95 CI) Specificit, % (%95 CI) PPV, % (%95 CI) NPV, % (%95 CI) 5% <0,001 100 (93,3 – 100) 47,1 (38,5 – 55,8) 42,1 (38,3 – 45,9) 100 Benign 65 - Malignant 126 53 (42,1) 10% 96,2 (87 – 99,5) 63,7 (55,2– 71,7) 50,5 (44,8 – 56,2) 97,8 (91,8 – 99,4) 90 2 (2,2) 101 51 (50,5) 15% 73,2 (65 – 80,4) 57,9 (51 – 64,6) 98,1 (92,8– 99,5) 103 2 (1,9) 88 51 (58)

16 RMI I in the literature…

17 N SEN SPE PPV NPV Yamamoto et al [161] ,7 79,4 91,2 Simsek et al 2014 [162] Arun-Muthuvel et al., 2014 [163] Terzic et al., 2013 [164] Sayasneh et al., 2013 [165] Van Gorp et al., 2012 [166] Ashrafgangooei et al., 2011 [167] Akker et al., 2010 [168] Yamamoto et al., 2009 [13] Obeidat et al., 2004 [169] Andersen et al., [170] Ma et al., 2003 [157] Manjunath et al., 2000 [156] Tingulstad et al., 1999 [12] Davies et al., [171] Jacobs et al., 1990 [10] Our Study ,4 68,6 87,1

18 CONCLUSION Results of versions of RMI were similar to those in the literature in terms of specifity, but lower in terms of sensitivity. Sensitivity and specifity values for ADNEX model were similar to those of the literature. Although sensitivity of ADNEX model were higher than those of versions of RMI, specifity values were lower. Future prospective studies could be performed to evaluate diagnostic perfomances of those indices and to develop newer indices in terms of better SEN and SPE.


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