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

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

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.

• Jezik: Angleški jezik
• Ključne besede: adnexal masses, diagnostic models, machine learning, ovarian cancer, steroids
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: MF - Medicinska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 11 strr.
• Številčenje: Vol. 25, no. 1, art. 410
• PID: 20.500.12556/RUL-187095
• UDK: 618.1-006:577.2
• ISSN pri članku: 1475-2867
• DOI: 10.1186/s12935-025-04047-8
• COBISS.SI-ID: 258923011
• Datum objave v RUL: 09.09.2026

Gradivo je del revije

• Naslov: Cancer cell international
• Skrajšan naslov: Cancer cell int.
• Založnik: Springer Nature
• ISSN: 1475-2867
• COBISS.SI-ID: 2594836

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: adneksalne mase, diagnostični modeli, strojno učenje, rak ovarijev, steroidi