A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses

Gjorgoska, Marija (author), Pirš, Boštjan (author), Smrkolj, Špela (author), Lanišnik-Rižner, Tea (author)

Background: Ovarian cancer is the deadliest gynecological malignancy, largely due to the advanced stage at diagnosis in most patients. This study investigates whether systemic steroids can serve as biomarkers to distinguish malignant ovarian tumors from non-malignant adnexal masses. Methods: This prospective, single-center observational study included 99 women with adnexal masses who underwent surgery between December 2021 and February 2025. Preoperative serum levels of 17 steroid hormones, including androgens, 11-oxyandrogens, glucocorticoids, and mineralocorticoids, were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Machine learning was employed to assess the diagnostic potential of these steroids in distinguishing ovarian cancer (n = 43) from non-malignant adnexal masses (n = 56). Results: Patients with ovarian cancer had lower levels of 11β-hydroxy-testosterone (11OHT), 11-keto-testosterone (11KT), and testosterone compared to controls. Using stepwise feature selection, we developed two diagnostic models incorporating three 11-oxyandrogens (11KT, 11OHT, and 11β-hydroxy-androstenedione), patient age, and either cancer antigen 125 (CA-125) or human epididymis protein 4 (HE4) for distinguishing malignant from non-malignant adnexal masses. The model including CA-125 achieved AUC of 0.907, 88.9% sensitivity and 82.0% specificity, while the model including HE4 achieved AUC of 0.911, 94.4% sensitivity and 77.3% specificity as evaluated by cross-validation. Both models significantly outperformed CA-125, HE4, and the Risk of Ovarian Malignancy Algorithm (ROMA) index alone. Conclusion: Patients with ovarian cancer exhibit distinct steroid profiles compared to those with non-malignant adnexal masses. If validated, the models could enhance diagnosis, reducing unnecessary surgeries for benign conditions while ensuring timely treatment for ovarian cancer, particularly when conventional biomarkers are inconclusive.

• Language: English
• Keywords: adnexal masses, diagnostic models, machine learning, ovarian cancer, steroids
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: MF - Faculty of Medicine
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 11 strr.
• Numbering: Vol. 25, no. 1, art. 410
• PID: 20.500.12556/RUL-187095
• UDC: 618.1-006:577.2
• ISSN on article: 1475-2867
• DOI: 10.1186/s12935-025-04047-8
• COBISS.SI-ID: 258923011
• Publication date in RUL: 09.09.2026

Record is a part of a journal

• Title: Cancer cell international
• Shortened title: Cancer cell int.
• Publisher: Springer Nature
• ISSN: 1475-2867
• COBISS.SI-ID: 2594836

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: adneksalne mase, diagnostični modeli, strojno učenje, rak ovarijev, steroidi