Assessing the diagnostic performance of four ovarian malignancy prediction risk models in differentiating benign and malignant ovarian masses in a tertiary hospital
- Author:
Mea Janelle F. Sarmiento‑Babiera
1
;
Erlidia F. Llamas‑Clark
1
Author Information
- Publication Type:Journal Article
- Keywords: Copenhagen Index; HE4; International Ovarian Tumor Analysis‑Assessment of Different NEoplasias in the AdneXa; Risk of Malignancy Index
- MeSH: Ovarian Neoplasms; Roma; Humans; Female
- From: Philippine Journal of Obstetrics and Gynecology 2022;46(5):193-201
- CountryPhilippines
- Language:English
-
Abstract:
Introduction:Ovarian cancer is considered the most lethal gynecologic malignancy because it is difficult to diagnose in its early stages. Ovarian malignancy prediction models may be useful in discriminating between benign and malignant masses, allowing for accurate and timely referral as well as proper therapeutic care
Objective:To evaluate the diagnostic performance of the four ovarian prediction models: Risk of Malignancy Index‑4 (RMI‑4), Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH‑I), and International Ovarian Tumor Analysis (IOTA)‑Assessment of Different NEoplasias in the AdneXa (ADNEX) in identifying malignant and benign ovarian masses
Materials and Methods:This was a retrospective, cross‑sectional, analytical diagnostic study in a tertiary hospital between January 2017 and December 2020. Receiver operating characteristic (ROC) curves, area under the curves (AUCs), sensitivities, specificities, positive and negative predictive values, and positive and negative likelihood ratios were used to assess the diagnostic performance of the prediction models.
Results:We analyzed a total of 248 patients. One hundred and sixty‑one (65%) had benign tumors, 28 (11%) had borderline, and 59 (24%) had malignant tumors. The AUCs of all models were all above 90%, but when compared to the other models, CPH‑I had the best estimate. RMI‑4 had the highest sensitivity (98.3%) in diagnosing malignancy. For appropriately diagnosing benign disease, the IOTA‑ADNEX model exhibited the highest specificity (92.1%). Overall, RMI‑4 had the lowest diagnostic accuracy (74.6%), whereas IOTA‑ADNEX had the greatest (93.2%).
Conclusion:The four malignancy prediction models in this study were all useful tools in discriminating between benign and malignant ovarian tumors. IOTA‑ADNEX, CPH‑I, and ROMA all demonstrated overlapping diagnostic performances indicating that they are equal in that regard. In terms of sensitivity in predicting malignancy, RMI‑4 was the most sensitive. CPH‑I is the predictor with the best overall estimate. Lastly, IOTA‑ADNEX was the most specific, and displayed highest diagnostic accuracy among the four - Full text:PhilippJObstetGynecol465193-1064994_001744.pdf
