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

Christina Pamporaki*, Annika M A Berends, Angelos Filippatos, Tamara Prodanov, Leah Meuter, Alexander Prejbisz, Felix Beuschlein, Martin Fassnacht, Henri J L M Timmers, Svenja Nölting, Kaushik Abhyankar, Georgiana Constantinescu, Carola Kunath, Robbert J de Haas, Katharina Wang, Hanna Remde, Stefan R Bornstein, Andrzeij Januszewicz, Mercedes Robledo, Jacques W M LendersMichiel N Kerstens, Karel Pacak, Graeme Eisenhofer

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

7 Citaten (Scopus)
19 Downloads (Pure)


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<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.

INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.

FUNDING: Deutsche Forschungsgemeinschaft.

Originele taal-2English
Pagina's (van-tot)e551-e559
Aantal pagina's9
TijdschriftLancet digital health
Nummer van het tijdschrift9
Vroegere onlinedatum18-jul.-2023
StatusPublished - sep.-2023

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