Prediction of metastatic pheochromocytoma and paraganglioma - a machine learning modelling study using data from a cross-sectional cohort

Aug 3, 2026

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Background: Pheochromocytomas and paragangliomas can progress to metastatic disease in up to 20% of cases, yet reliable prediction remains difficult. Methods: A prospective cross-sectional cohort from the PMT trial evaluated methoxytyramine for metastatic prediction in 267 patients, with model training and validation using an additional retrospective dataset (n=493) and external validation across all PMT participants. Specialist predictions were also collected for comparison. Findings: Methoxytyramine alone showed limited sensitivity (52%) with higher specificity (85%). Machine learning improved performance. Interpretation: A model using nine features offers a preoperative approach to guide individualized management and follow-up.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Summary

Background

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Funding

University of Groningen Prediction of metastatic pheochromocytoma and paraganglioma Pamporaki, Christina; Berends, Annika M A; Filippatos, Angelos; Prodanov, Tamara; Meuter, Leah; Prejbisz, Alexander; Beuschlein, Felix; Fassnacht, Martin; Timmers, Henri J L M; Nölting, Svenja Published in: Lancet digital health DOI: 10.1016/S2589-7500(23)00094-8 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2023 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Pamporaki, C. , Berends, A. M. A. , Filippatos, A. , Prodanov, T. , Meuter, L. , Prejbisz, A. , Beuschlein, F. , Fassnacht, M. , Timmers, H. J. L. M. , Nölting, S. , Abhyankar, K. , Constantinescu, G. , Kunath, C. , de Haas, R. J. , Wang, K. , Remde, H. , Bornstein, S. R. , Januszewicz, A. , Robledo, M. , ... Eisenhofer, G. (2023) . Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort. Lancet digital health, 5(9), e551-e559 . [https://doi.org/10.1016/S2589-](https://doi.org/10.1016/S2589-)[ ](https://doi.org/10.1016/S2589-)[7500](7500)([23](23))[00094-8](00094-8) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 02-08-2026 Articles Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort Christina Pamporaki, Annika MA Berends*, Angelos Filippatos*, Tamara Prodanov, Leah Meuter, Alexander Prejbisz, Felix Beuschlein, Martin Fassnacht, Henri J L M Timmers, Svenja Nölting, KaushikAbhyankar, Georgiana Constantinescu, Carola Kunath, RobbertJ de Haas, Katharina Wang, Hanna Remde, Stefan R Bornstein, Andrzeij Januszewicz, Mercedes Robledo, Jacques WM Lenders, Michiel N Kerstens, Karel Pacak, Graeme Eisenhofer Summary Background Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. Methods In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical tes

FAQs

What clinical problem does this study address?

The study addresses the difficulty of reliably predicting metastatic disease in patients with pheochromocytoma and paraganglioma, which can occur in up to 20% of cases.

How did the researchers evaluate methoxytyramine for metastatic prediction?

Methoxytyramine was assessed using cross-sectional cohort data from the PMT trial prospectively, then machine learning models were trained and validated using additional features from retrospective datasets.

How does the best machine learning model compare with specialist predictions?

The best model achieved stronger discrimination (AUC 0.942) than the best specialist before and after SDHB variant data provision, and showed high sensitivity and specificity in external validation.

Aug 3, 2026

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