20.500.12556/DiRROS-28873 A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses 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. adnexal masses diagnostic models machine learning ovarian cancer steroids adneksalne mase diagnostični modeli strojno učenje rak ovarijev steroidi true false true Angleški jezik Slovenski jezik Neznano 2026-04-10 11:11:49 2026-04-10 11:11:49 2026-04-11 03:54:48 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 27. 11. 2025; str. 1-11 no. 1, [article no.] 410 Vol. 25 Nov. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 618.1-006:577.2 1475-2867 10.1186/s12935-025-04047-8 258923011 RAZ_Gjorgoska_Marija_2025.pdf RAZ_Gjorgoska_Marija_2025.pdf 1 4CA7AFA2DDE81989A62B7BAEB93E2D9A 6a659cc4a72b03c3262e887f395c2b1d649fcfd5e080bb84c4bda4ac287668ee 6e0ee899-34bd-11f1-b941-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43043 https://link.springer.com/article/10.1186/s12935-025-04047-8 1 51b3aa6c-34bd-11f1-b941-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43042 Univerzitetni klinični center Ljubljana 0 0 0