@article{ArltBiehlTayloretal.2011,
  author    = {Arlt, Wiebke and Biehl, Michael and Taylor, Angela E. and Hahner, Stefanie and Lib{\´e}, Rossella and Hughes, Beverly A. and Schneider, Petra and Smith, David J. and Stiekema, Han and Krone, Nils and Porfiri, Emilio and Opocher, Giuseppe and Bertherat, Jer{\^o}me and Mantero, Franco and Allolio, Bruno and Terzolo, Massimo and Nightingale, Peter and Shackleton, Cedric H. L. and Bertagna, Xavier and Fassnacht, Martin and Stewart, Paul M.},
  title     = {Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors},
  series = {The Journal of Clinical Endocrinology \& Metabolism},
  volume    = {96},
  journal   = {The Journal of Clinical Endocrinology \& Metabolism},
  number    = {12},
  doi       = {10.1210/jc.2011-1565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154682},
  pages     = {3775 -- 3784},
  year      = {2011},
  abstract  = {Context: Adrenal tumors have a prevalence of around 2\% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11\% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90\% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88\% (area under the curve = 0.96) when using only the nine most differentiating markers. Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.},
  language  = {en}
}
@article{CanuPuglisiBerchiallaetal.2021,
  author    = {Canu, Letizia and Puglisi, Soraya and Berchialla, Paola and De Filpo, Giuseppina and Brignardello, Francesca and Schiavi, Francesca and Ferrara, Alfonso Massimiliano and Zovato, Stefania and Luconi, Michaela and Pia, Anna and Appetecchia, Marialuisa and Arvat, Emanuela and Letizia, Claudio and Maccario, Mauro and Parasiliti-Caprino, Mirko and Altieri, Barbara and Faggiano, Antongiulio and Modica, Roberta and Morelli, Valentina and Arosio, Maura and Verga, Uberta and Pellegrino, Micaela and Petramala, Luigi and Concistr{\`e}, Antonio and Razzore, Paola and Ercolino, Tonino and Rapizzi, Elena and Maggi, Mario and Stigliano, Antonio and Burrello, Jacopo and Terzolo, Massimo and Opocher, Giuseppe and Mannelli, Massimo and Reimondo, Giuseppe},
  title     = {A multicenter epidemiological study on second malignancy in non-syndromic pheochromocytoma/paraganglioma patients in Italy},
  series = {Cancers},
  volume    = {13},
  journal   = {Cancers},
  number    = {22},
  issn      = {2072-6694},
  doi       = {10.3390/cancers13225831},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250148},
  year      = {2021},
  abstract  = {No studies have carried out an extensive analysis of the possible association between non-syndromic pheochromocytomas and paragangliomas (PPGLs) and other malignancies. To assess >the risk of additional malignancy in PPGL, we retrospectively evaluated 741 patients with PPGLs followed-up in twelve referral centers in Italy. Incidence of second malignant tumors was compared between this cohort and Italian patients with two subsequent malignancies. Among our patients, 95 (12.8\%) developed a second malignant tumor, which were mainly prostate, colorectal and lung/bronchial cancers in males, breast cancer, differentiated thyroid cancer and melanoma in females. The standardized incidence ratio was 9.59 (95\% CI 5.46-15.71) in males and 13.21 (95\% CI 7.52-21.63) in females. At multivariable analysis, the risk of developing a second malignant tumor increased with age at diagnosis (HR 2.50, 95\% CI 1.15-5.44, p = 0.021 for 50-59 vs. <50-year category; HR 3.46, 95\% CI 1.67-7.15, p < 0.001 for >60- vs. <50-year). In patients with available genetic evaluation, a positive genetic test was inversely associated with the risk of developing a second tumor (HR 0.25, 95\% CI 0.10-0.63, p = 0.003). In conclusion, PPGLs patients have higher incidence of additional malignant tumors compared to the general population who had a first malignancy, which could have an impact on the surveillance strategy.},
  language  = {en}
}