Comparison of sensitivity for Risk of Ovarian Malignancy Algorithm (ROMA) and Assessment of Different NEoplasias in the adneXa (ADNEX) model for predicting ovarian cancer in a woman with adnexal masses

医学 妇科 卵巢癌 恶性肿瘤 算法 癌症 肿瘤科 产科 内科学 计算机科学
作者
Pakorn Tangjanyatham,Woraphot Chaowawanit
出处
期刊:International Journal of Gynecological Cancer [BMJ]
卷期号:35 (6): 101827-101827 被引量:3
标识
DOI:10.1016/j.ijgc.2025.101827
摘要

This study aimed to compare the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA) and the Assessment of Different NEoplasias in the adneXa (ADNEX) model in predicting ovarian cancer in women presenting with adnexal masses METHODS: A prospective diagnostic study was conducted at the Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Thailand. A total of 110 women with adnexal masses confirmed by ultrasound were enrolled. Pre-operative transvaginal ultrasound findings, serum CA125, and HE4 levels were used to evaluate the diagnostic performance of the ROMA and ADNEX models, with histopathological examination as the reference standard. The ADNEX model applied a 10% malignancy risk cutoff. Using a 10% cutoff, the ADNEX model achieved a sensitivity of 91.9% and a specificity of 65.7%. In comparison, ROMA demonstrated a sensitivity of 64.8% and a specificity of 86.3%. The combined use of ADNEX and ROMA did not significantly improve diagnostic specificity. The receiver operating characteristic analysis for the ADNEX model showed an area under the curve of 0.83, indicating good diagnostic accuracy. The optimal threshold for malignancy risk was identified at a 13.8% cutoff, balancing sensitivity and specificity. The ADNEX model, with a 10% malignancy risk cutoff, provides superior sensitivity in diagnosing ovarian cancer in adnexal mass cases and could significantly contribute to early detection strategies. However, its lower specificity highlights the need for cautious interpretation. Further studies are warranted to refine these models and enhance their applicability across diverse clinical environments.
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