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Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma
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 ste...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112967/ https://www.ncbi.nlm.nih.gov/pubmed/31665449 http://dx.doi.org/10.1210/clinem/dgz141 |
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author | Chortis, Vasileios Bancos, Irina Nijman, Thomas Gilligan, Lorna C Taylor, Angela E Ronchi, Cristina L O’Reilly, Michael W Schreiner, Jochen Asia, Miriam Riester, Anna Perotti, Paola Libé, 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, Jonathan J Biehl, Michael Arlt, Wiebke |
author_facet | Chortis, Vasileios Bancos, Irina Nijman, Thomas Gilligan, Lorna C Taylor, Angela E Ronchi, Cristina L O’Reilly, Michael W Schreiner, Jochen Asia, Miriam Riester, Anna Perotti, Paola Libé, 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, Jonathan J Biehl, Michael Arlt, Wiebke |
author_sort | Chortis, Vasileios |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7112967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71129672020-04-06 Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma Chortis, Vasileios Bancos, Irina Nijman, Thomas Gilligan, Lorna C Taylor, Angela E Ronchi, Cristina L O’Reilly, Michael W Schreiner, Jochen Asia, Miriam Riester, Anna Perotti, Paola Libé, 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, Jonathan J Biehl, Michael Arlt, Wiebke J Clin Endocrinol Metab Online Only 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. Oxford University Press 2019-10-29 /pmc/articles/PMC7112967/ /pubmed/31665449 http://dx.doi.org/10.1210/clinem/dgz141 Text en © Endocrine Society 2019. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Online Only Chortis, Vasileios Bancos, Irina Nijman, Thomas Gilligan, Lorna C Taylor, Angela E Ronchi, Cristina L O’Reilly, Michael W Schreiner, Jochen Asia, Miriam Riester, Anna Perotti, Paola Libé, 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, Jonathan J Biehl, Michael Arlt, Wiebke Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title | Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title_full | Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title_fullStr | Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title_full_unstemmed | Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title_short | Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma |
title_sort | urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma |
topic | Online Only |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112967/ https://www.ncbi.nlm.nih.gov/pubmed/31665449 http://dx.doi.org/10.1210/clinem/dgz141 |
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