Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma

Research output: Contribution to journalArticlepeer-review

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Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma. / Chortis, Vasileios; Bancos, Irina; Nijman, Thomas ; Gilligan, Lorna; Taylor, Angela; Ronchi, Cristina; O'Reilly, Michael; Schreiner, Jochen ; Asia, Miriam; Riester, Anna; Perotti, Paola; Libe, Rosella; Quinkler, Marcus; Canu, Letizia; Paiva, Isabel; Bugalho, Maria J ; Kastelan, Darko; Dennedy, M Conall; Sherlock, Mark ; Ambroziak, Urszula; Vassiliadi, Dimitra ; Bertherat, Jerome; Beuschlein, Felix; Fassnacht, Martin; Deeks, Jon; Biehl, Michael; Arlt, Wiebke.

In: Journal of Clinical Endocrinology and Metabolism, Vol. 105, No. 3, 03.2020, p. e303–e314.

Research output: Contribution to journalArticlepeer-review

Harvard

Chortis, V, Bancos, I, Nijman, T, Gilligan, L, Taylor, A, Ronchi, C, O'Reilly, M, Schreiner, J, Asia, M, Riester, A, Perotti, P, Libe, R, Quinkler, M, Canu, L, Paiva, I, Bugalho, MJ, Kastelan, D, Dennedy, MC, Sherlock, M, Ambroziak, U, Vassiliadi, D, Bertherat, J, Beuschlein, F, Fassnacht, M, Deeks, J, Biehl, M & Arlt, W 2020, 'Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma', Journal of Clinical Endocrinology and Metabolism, vol. 105, no. 3, pp. e303–e314. https://doi.org/10.1210/clinem/dgz141

APA

Chortis, V., Bancos, I., Nijman, T., Gilligan, L., Taylor, A., Ronchi, C., O'Reilly, M., Schreiner, J., Asia, M., Riester, A., Perotti, P., Libe, R., Quinkler, M., Canu, L., Paiva, I., Bugalho, M. J., Kastelan, D., Dennedy, M. C., Sherlock, M., ... Arlt, W. (2020). Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma. Journal of Clinical Endocrinology and Metabolism, 105(3), e303–e314. https://doi.org/10.1210/clinem/dgz141

Vancouver

Author

Chortis, Vasileios ; Bancos, Irina ; Nijman, Thomas ; Gilligan, Lorna ; Taylor, Angela ; Ronchi, Cristina ; O'Reilly, Michael ; Schreiner, Jochen ; Asia, Miriam ; Riester, Anna ; Perotti, Paola ; Libe, Rosella ; Quinkler, Marcus ; Canu, Letizia ; Paiva, Isabel ; Bugalho, Maria J ; Kastelan, Darko ; Dennedy, M Conall ; Sherlock, Mark ; Ambroziak, Urszula ; Vassiliadi, Dimitra ; Bertherat, Jerome ; Beuschlein, Felix ; Fassnacht, Martin ; Deeks, Jon ; Biehl, Michael ; Arlt, Wiebke. / Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma. In: Journal of Clinical Endocrinology and Metabolism. 2020 ; Vol. 105, No. 3. pp. e303–e314.

Bibtex

@article{f226afe7dc03403799555fea78cfea5b,
title = "Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma",
abstract = "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 post-operative recurrence detection after microscopically complete (R0) resection of ACC.Patients and Methods:135 patients from 14 clinical centers provided post-operative 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 three blinded experts detected recurrence by the time of radiological diagnosis in 50-72% of cases, improving to 69-92%, if a pre-operative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22-39% 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 post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.",
keywords = "Adrenocortical carcinoma, ACC, Steroid metabolomics, Mass spectrometry, Machine learning, Recurrence detection",
author = "Vasileios Chortis and Irina Bancos and Thomas Nijman and Lorna Gilligan and Angela Taylor and Cristina Ronchi and Michael O'Reilly and Jochen Schreiner and Miriam Asia and Anna Riester and Paola Perotti and Rosella Libe and Marcus Quinkler and Letizia Canu and Isabel Paiva and Bugalho, {Maria J} and Darko Kastelan and Dennedy, {M Conall} and Mark Sherlock and Urszula Ambroziak and Dimitra Vassiliadi and Jerome Bertherat and Felix Beuschlein and Martin Fassnacht and Jon Deeks and Michael Biehl and Wiebke Arlt",
year = "2020",
month = mar,
doi = "10.1210/clinem/dgz141",
language = "English",
volume = "105",
pages = "e303–e314",
journal = "Journal of Clinical Endocrinology and Metabolism",
issn = "0021-972X",
publisher = "Endocrine Society",
number = "3",

}

RIS

TY - JOUR

T1 - Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma

AU - Chortis, Vasileios

AU - Bancos, Irina

AU - Nijman, Thomas

AU - Gilligan, Lorna

AU - Taylor, Angela

AU - Ronchi, Cristina

AU - O'Reilly, Michael

AU - Schreiner, Jochen

AU - Asia, Miriam

AU - Riester, Anna

AU - Perotti, Paola

AU - Libe, Rosella

AU - Quinkler, Marcus

AU - Canu, Letizia

AU - Paiva, Isabel

AU - Bugalho, Maria J

AU - Kastelan, Darko

AU - Dennedy, M Conall

AU - Sherlock, Mark

AU - Ambroziak, Urszula

AU - Vassiliadi, Dimitra

AU - Bertherat, Jerome

AU - Beuschlein, Felix

AU - Fassnacht, Martin

AU - Deeks, Jon

AU - Biehl, Michael

AU - Arlt, Wiebke

PY - 2020/3

Y1 - 2020/3

N2 - 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 post-operative recurrence detection after microscopically complete (R0) resection of ACC.Patients and Methods:135 patients from 14 clinical centers provided post-operative 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 three blinded experts detected recurrence by the time of radiological diagnosis in 50-72% of cases, improving to 69-92%, if a pre-operative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22-39% 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 post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.

AB - 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 post-operative recurrence detection after microscopically complete (R0) resection of ACC.Patients and Methods:135 patients from 14 clinical centers provided post-operative 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 three blinded experts detected recurrence by the time of radiological diagnosis in 50-72% of cases, improving to 69-92%, if a pre-operative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22-39% 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 post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.

KW - Adrenocortical carcinoma

KW - ACC

KW - Steroid metabolomics

KW - Mass spectrometry

KW - Machine learning

KW - Recurrence detection

U2 - 10.1210/clinem/dgz141

DO - 10.1210/clinem/dgz141

M3 - Article

C2 - 31665449

VL - 105

SP - e303–e314

JO - Journal of Clinical Endocrinology and Metabolism

JF - Journal of Clinical Endocrinology and Metabolism

SN - 0021-972X

IS - 3

ER -