Jimmy Rusdian Masjkur
Universitätsklinikum Carl Gustav Carus Dresden; Medizinische Klinik und Poliklinik III
A novel tool for the diagnosis of autonomous cortisol secretion
The diagnosis of neoplastic Cushing’s syndrome, particularly autonomous cortisol secretion (ACS) represents a challenge due to limitations of the currently used diagnostic tests and differences in the definition of clinically relevant disease. In recent studies, we have shown that patients with different subtypes of Cushing’s syndrome have different plasma steroid profiles and that would serve as an additional single-test alternative for screening purposes. Applications of artificial intelligence, including machine learning generated from the steroid-fingerprints, are gaining increasing recognition for informing medical decision-making. Machine learning may be particularly useful in heterogeneous disorders where there is a need for stratification to guide therapy.
The main aim of this project is to provide an alternative simple one way method to diagnose endogenous hypercortisolism to the already established but much more complex routine diagnostic tests. With steroid fingerprints and associated patient data (e.g., age, sex, comorbidities, medications) we plan to include the application of artificial intelligence-based machine learning approaches to develop algorithms for simple and rapid diagnostic prediction of disease subtypes in larger prospective cohort.
Data collection of Biospecimen from 246 Patients (54 patients with well-defined ACS and 192 patients with non-functional adrenal adenomas as the control group) analysed for a panel of 14 plasma steroids by means of LC-MS/MS-based targeted steroid profiling was competed. Our first priority will be to finalize the process on generating the machine-learning (ML) algorithm in cooperation with our IT Department at TU Dresden. Data from patients recruited in München and Würzburg will be used to evaluate the accuracy and efficiency of the Tool.
Masjkur J, Barthel A, Kanczkowski W, Müller G, Bornstein SR. Practical recommendations for screening and management of functional disorders of the adrenal cortex in cases of SARS-CoV-2 infection. Internist (Berl). 2022;63(1):4-11.2. https://pubmed.ncbi.nlm.nih.gov/34928398/
Berke K, Constantinescu G, Masjkur J, Kimpel O, Dischinger U, Peitzsch M, Kwapiszewska A, Dobrowolski P, Nölting S, Reincke M, Beuschlein F, Bornstein SR, Prejbisz A, Lenders JWM, Fassnacht M, Eisenhofer G. Plasma steroid profiling in patients with adrenal incidentaloma. J Clin Endocrinol Metab. 2022;107(3):e1181-e1192. https://pubmed.ncbi.nlm.nih.gov/34665854/
Prete A, Subramanian A, Bancos I, Chortis V, Tsagarakis S, Lang K, Macech M, Delivanis DA, Pupovac ID, Reimondo G, Marina LV, Deutschbein T, Balomenaki M, O’Reilly MW, Gilligan LC, Jenkinson C, Bednarczuk T, Zhang CD, Dusek T, Diamantopoulos A, Asia M, Kondracka A, Li D, Masjkur JR, Quinkler M, Ueland GÅ, Dennedy MC, Beuschlein F, Tabarin A, Fassnacht M, Ivović M, Terzolo M, Kastelan D, Young WF Jr, Manolopoulos KN, Ambroziak U, Vassiliadi DA, Taylor AE, Sitch AJ, Nirantharakumar K, Arlt W. Cardiometabolic disease burden and steroid excretion in benign adrenal tumors : A cross-sectional multicenter study. Ann Intern Med 2022. doi: 10.7326/M21-1737. https://pubmed.ncbi.nlm.nih.gov/34978855/
Remde H, Pamporaki C, Quinkler M, Nölting S, Prejbisz A, Timmers HJ, Masjkur J, Fuss CT, Fassnacht M, Eisenhofer G, Deutschbein T. Improved diagnostic accuracy of clonidine suppression testing using an age-related cutoff for plasma normetanephrine. Hypertension 2022 Jun;79(6):1257-1264. https://pubmed.ncbi.nlm.nih.gov/35378989/
Hannah-Shmouni F, Berthon A, Faucz FR, Briceno JM, Maria AG, Demidowich A, Peitzsch M, Masjkur J, Bonnet-Serrano F, Vaczlavik A, Bertherat J, Reincke M, Eisenhofer G, Stratakis CA. Mass spectrometry-based steroid profiling in primary bilateral macronodular adrenocortical hyperplasia. Endocr Relat Cancer. 2020;27(7):403-413. https://pubmed.ncbi.nlm.nih.gov/32348959/