Evaluating Efficacy: The ADNEX Model as a Predictive Tool for Ovarian Masses

Abstract

Ovarian cancer ranks as the eighth most common cancer and the seventh leading cause of cancer-related deaths among women globally. In 2022, there were approximately 321,476 new cases and 248,305 deaths due to this malignancy. Often labeled as "silent killers," ovarian tumors present with vague symptoms, leading to late diagnoses. The current lack of effective screening methods necessitates reliance on radiological imaging and tumor markers like CA-125 for evaluation and diagnosis. The IOTA group's ADNEX model, introduced in 2014, represents a significant advancement in risk assessment for differentiating between benign and malignant ovarian tumors.

Aim

This study aims to evaluate the predictive accuracy of the ADNEX model for ovarian masses.

Material and Methods

Conducted over 18 months at Sree Balaji Medical College and Hospital in Chennai, India, this observational diagnostic study included 196 women presenting with adnexal masses. Exclusion criteria encompassed prior malignancy treatment and pregnancy. Data on demographic and clinical parameters were collected, followed by ultrasonography and CA-125 assessments. The ADNEX algorithm was applied to predict malignancy risk, and surgical specimens underwent histopathological evaluation. Statistical analyses, including ROC curve assessments, were performed using IBM SPSS software.

Results

The study population predominantly consisted of women aged 41-50 years, with benign lesions representing 70.9% and malignant lesions 18.3% of cases. CA-125 levels were below 35 IU/mL in 74% of participants. The ADNEX model showed high sensitivity (91.67%) and specificity (76.25%) in malignancy detection, with an area under the ROC curve of 0.933.

Discussion

The results align with previous studies confirming the ADNEX model's robust performance in distinguishing malignant from benign ovarian neoplasms. While the model demonstrated a strong sensitivity, it indicated a higher propensity for false positives, suggesting a need for further refinement in clinical settings.

Conclusion

The ADNEX model is a valuable tool for assessing ovarian masses, providing nuanced risk stratification that enhances clinical decision-making. Future research should explore its diagnostic accuracy across different ovarian cancer subtypes to validate its comprehensive applicability.