20.500.12556/DiRROS-28875 Multi-steroid profiling and machine learning reveal androgens as candidate biomarkers for endometrial cancer diagnosis a case-control study Objective: To evaluate the diagnostic and prognostic potential of preoperative serum steroid levels in endometrial cancer (EC) alone and in combination with clinical parameters and biomarkers CA-125 and HE4. Methods: This single-center observational study included 62 patients with EC and 70 controls with benign uterine conditions who underwent surgery between June 2012 and February 2020. Preoperative serum levels of classic androgens, 11-oxyandrogens, glucocorticoids and mineralocorticoids were measured using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Machine learning was used to assess their diagnostic and prognostic value alone and combined with clinical parameters and tumor biomarkers. Results: Patients with EC had significantly higher serum levels of classic androgens (androstenedione, testosterone), 11-oxyandrogens (11β-hydroxy-androstenedione, 11β-hydroxy-testosterone) and glucocorticoids (17α-hydroxy-progesterone, 11-deoxycortisol) compared to controls. While individual steroids had limited diagnostic value, a multivariate model including classic androgens, CA-125, HE4, BMI and parity achieved an AUC 0.87, 79.1% sensitivity and 74.7% specificity in distinguishing EC from benign uterine condition. This model outperformed our previously published model based on CA-125, HE4 and BMI (AUC: 0.81, p < 0.0001). Prognostically, HE4 was the strongest marker for lymphovascular space invasion (LVSI) (AUC: 0.79) and deep myometrial invasion (MI) (AUC: 0.71). Among steroids, androstenedione was the most predictive of LVSI (AUC: 0.67), while 11β-hydroxy-testosterone was the strongest predictor of deep MI (AUC: 0.64). Conclusions: Patients with EC exhibit distinct steroid hormone profiles. While steroids alone offer modest diagnostic and prognostic value, integrating them into multivariate models improves diagnostic accuracy. endometrial cancer multi-steroid profiling liquid chromatography–tandem mass spectrometry machine learning diagnosis prognosis rak endometrija večsteroidno profiliranje tekočinska kromatografija–tandemska masna spektrometrija strojno učenje diagnoza prognoza true false true Angleški jezik Slovenski jezik Neznano 2026-04-10 11:25:45 2026-04-10 11:25:46 2026-04-11 03:54:48 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 9. 6. 2025; str. 1-18 iss. 10, [article no.] 1679 Vol. 17 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 618.1-006:577.2 2072-6694 10.3390/cancers17101679 238728963 RAZ_Gjorgoska_Marija_2025.pdf RAZ_Gjorgoska_Marija_2025.pdf 1 BDB13F8D276757CA14790C1C22B5E065 eba530bc4676d917ac483b735df29a6a828fbbd590ab9d7531b579b9ed6591ce 5d160321-34bf-11f1-b941-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43047 https://www.mdpi.com/2072-6694/17/10/1679 1 43619713-34bf-11f1-b941-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43046 Univerzitetni klinični center Ljubljana 0 0 0