4.6 Article

Diagnostic Added-Value of Serum CA-125 on the IOTA Simple Rules and Derivation of Practical Combined Prediction Models (IOTA SR X CA-125)

Journal

DIAGNOSTICS
Volume 11, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/diagnostics11020173

Keywords

ovarian cancer; diagnosis; international ovarian tumor analysis (IOTA); ultrasound; CA-125

Funding

  1. Chiang Mai University Research Fund [CMU-2564]

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The study demonstrated that the model combining IOTA Simple Rules and serum CA-125 information was significantly superior in predicting the malignant potential of ovarian tumors compared to using only IOTA Simple Rules. This model, known as the IOTA SR X CA-125 model, showed high discriminative ability and could potentially aid in guiding patient referrals to oncologic specialists.
Background: This study aimed to evaluate the diagnostic added-value of serum CA-125 to the International Ovarian Tumor Analysis (IOTA) Simple Rules in order to facilitate differentiation between malignant and benign ovarian tumors before surgery. Methods: A secondary analysis of a cross-sectional cohort of women scheduled for surgery in Maharaj Nakorn Chiang Mai Hospital between April 2010 and March 2018 was carried out. Demographic and clinical data were prospectively collected. Histopathologic diagnosis was used as the reference standard. Logistic regression was used for development of the model. Evaluation of the diagnostic added-value was based on the increment of the area under the receiver operating characteristic curve (AuROC). Results: One hundred and forty-five women (30.3%) out of a total of 479 with adnexal masses had malignant ovarian tumors. The model that included information from the IOTA Simple Rules and serum CA-125 was significantly more superior to the model that used only information from the IOTA Simple Rules (AuROC 0.95 vs. 0.89, p < 0.001 for pre-menopause and AuROC 0.98 vs 0.83, p < 0.001 for post-menopause). Conclusions: The IOTA SR X CA-125 model showed high discriminative ability and is potentially useful as a decision tool for guiding patient referrals to oncologic specialists.

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