Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma
| Autor: | Angela E Taylor, Isabel Paiva, Dimitra Vassiliadi, Martin Fassnacht, Miriam Asia, Thomas Nijman, Maria João Bugalho, Letizia Canu, Urszula Ambroziak, Lorna C Gilligan, Jonathan J Deeks, Felix Beuschlein, Cristina L Ronchi, Darko Kastelan, Anna Riester, R. Libe, Marcus Quinkler, Michael W O'Reilly, Jochen Schreiner, Mark Sherlock, Paola Perotti, Jérôme Bertherat, Michael Biehl, M Conall Dennedy, Irina Bancos, Wiebke Arlt, Vasileios Chortis |
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| Přispěvatelé: | Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Endocrinology Diabetes and Metabolism medicine.medical_treatment Clinical Biochemistry Urine Biochemistry Machine Learning 0302 clinical medicine Endocrinology Recurrent Adrenocortical Carcinoma 80 and over Adrenocortical Carcinoma Adrenocortical carcinoma Longitudinal Studies Postoperative Period Tomography mass spectrometry Aged 80 and over Tumor ACC adrenocortical carcinoma machine learning recurrence detection steroid metabolomics Adrenal Cortex Adrenal Cortex Neoplasms Adrenalectomy Adult Aged Biomarkers Tumor Female Follow-Up Studies Gas Chromatography-Mass Spectrometry Humans Metabolomics Middle Aged Neoplasm Recurrence Local Proof of Concept Study Retrospective Studies Sensitivity and Specificity Steroids Tomography X-Ray Computed Young Adult 3. Good health X-Ray Computed Online Only Local 030220 oncology & carcinogenesis AcademicSubjects/MED00250 medicine.medical_specialty Urology 030209 endocrinology & metabolism Steroid 03 medical and health sciences Internal medicine medicine Clinical Research Articles R0 resection business.industry Biochemistry (medical) Retrospective cohort study medicine.disease Neoplasm Recurrence business Biomarkers |
| Zdroj: | The Journal of Clinical Endocrinology and Metabolism Journal of Clinical Endocrinology and Metabolism, 105(3):dgz141, 1-12. ENDOCRINE SOC |
| ISSN: | 1945-7197 0021-972X |
| Popis: | Context Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). Objective, Design, Setting This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. Patients and Methods 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography–mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. Results Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine “steroid fingerprint” at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). Conclusion Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence. |
| Databáze: | OpenAIRE |
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