Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort
| Autor: | Christina Pamporaki, PD, Annika M A Berends, PhD, Angelos Filippatos, ProfPhD, Tamara Prodanov, PhD, Leah Meuter, MD, Alexander Prejbisz, ProfPhD, Felix Beuschlein, ProfPhD, Martin Fassnacht, ProfPhD, Henri J L M Timmers, ProfPhD, Svenja Nölting, ProfPhD, Kaushik Abhyankar, Dipl-Ing, Georgiana Constantinescu, MD, Carola Kunath, Robbert J de Haas, PhD, Katharina Wang, Staatsexa, Hanna Remde, PhD, Stefan R Bornstein, ProfPhD, Andrzeij Januszewicz, ProfPhD, Mercedes Robledo, ProfPhD, Jacques W M Lenders, ProfPhD, Michiel N Kerstens, PhD, Karel Pacak, ProfDrSc, Graeme Eisenhofer, ProfPhD |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | The Lancet: Digital Health, Vol 5, Iss 9, Pp e551-e559 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2589-7500 23172576 |
| DOI: | 10.1016/S2589-7500(23)00094-8 |
| Popis: | 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 test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets. Findings: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894–0·969) that was larger (p |
| Databáze: | Directory of Open Access Journals |
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