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

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&lt;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.</p

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

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&lt;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.</p

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

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&lt;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.</p

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

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&lt;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.</p

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

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&lt;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.</p

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

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&lt;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.</p

Optimized procedures for testing plasma metanephrines in patients on hemodialysis

Diagnosis of pheochromocytomas and paragangliomas in patients receiving hemodialysis is troublesome. The aim of the study was to establish optimal conditions for blood sampling for mass spectrometric measurements of normetanephrine, metanephrine and 3-methoxytyramine in patients on hemodialysis and specific reference intervals for plasma metanephrines under the most optimal sampling conditions. Blood was sampled before and near the end of dialysis, including different sampling sites in 170 patients on hemodialysis. Plasma normetanephrine concentrations were lower (P < 0.0001) and metanephrine concentrations higher (P < 0.0001) in shunt than in venous blood, with no differences for 3-methoxytyramine. Normetanephrine, metanephrine and 3-methoxytyramine concentrations in shunt and venous blood were lower (P < 0.0001) near the end than before hemodialysis. Upper cut-offs for normetanephrine were 34% lower when the blood was drawn from the shunt and near the end of hemodialysis compared to blood drawn before hemodialysis. This study establishes optimal sampling conditions using blood from the dialysis shunt near the end of hemodialysis with optimal reference intervals for plasma metanephrines for the diagnosis of pheochromocytomas/paragangliomas among patients on hemodialysis

Case report: Incidentally discovered case of pheochromocytoma as a cause of long COVID-19 syndrome

Pheochromocytomas (PCCs) are rare but potentially lethal tumors that arise from the adrenal medulla. The clinical suspicion and diagnosis of PCC can be challenging due to the non-specific nature of signs and symptoms. In many patients, infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could lead to long-term symptoms including fatigue, headaches, and cognitive dysfunction. Here, we present the case of a patient incidentally diagnosed with an adrenal mass that proved to be a PCC after imaging was performed due to persisting complaints after coronavirus disease 2019 (COVID-19) infection. A 37-year-old male patient was referred to our center because of a right-sided inhomogeneous adrenal mass, incidentally found during a computed tomographic scan of the thorax performed due to cough and dyspnea that persisted after COVID-19 infection. Other complaints that were present prior to COVID-19 infection included profuse sweating, dizziness, exhaustion with chronic fatigue, and concentration difficulties. The patient had no history of hypertension, his blood pressure was normal, and the 24-h ambulatory blood pressure monitoring confirmed normotension but with the absence of nocturnal dipping. Plasma normetanephrine was 5.7-fold above the upper limit (UL) of reference intervals (738 pg/ml, UL = 129 pg/ml), whereas plasma metanephrine and methoxytyramine were normal at 30 pg/ml (UL = 84 pg/ml) and <4 pg/ml (UL = 16 pg/ml), respectively. Preoperative preparation with phenoxybenzamine was initiated, and a 4-cm tumor was surgically resected. Profuse sweating as well as dizziness was resolved after adrenalectomy pointing toward PCC and not COVID-19-associated patient concerns. Altogether, this case illustrates the difficulties in recognizing the possibility of PCC due to the non-specific nature of signs and symptoms of the tumor, which in this case did not include hypertension and coincided with some of the symptoms of long COVID-19

Mass spectrometry reveals misdiagnosis of primary aldosteronism with scheduling for adrenalectomy due to immunoassay interference

BACKGROUND Diagnosis of primary aldosteronism (PA) involves a multistep process reliant on the accuracy of aldosterone measurements at each step. We report on immunoassay interference leading to a wrongful diagnosis and indication for surgical intervention. CASE A 38-year old hypertensive male with a 1.4 cm left adrenal mass was diagnosed with PA based on an elevated aldosterone:renin ratio and a positive saline infusion test. Adrenal venous sampling (AVS) indicated left-sided aldosterone hypersecretion, supporting a decision to remove the left adrenal. The patient was also enrolled in a study to evaluate mass spectrometry-based steroid profiling, which indicated plasma aldosterone concentrations measured in five different peripheral samples averaging only 11% those of the immunoassay. Mass spectrometric measurements did not support left-sided adrenal aldosterone hypersecretion. Two independent laboratories confirmed differences in measurements by immunoassay and mass spectrometry. Lowered concentrations measured by the immunoassay that matched those by mass spectrometry were achieved after sample purification to remove macromolecules, confirming immunoassay interference. CONCLUSIONS Although our patient may represent an isolated case of immunoassay interference leading to misdiagnosis of PA, unnecessary AVS and potentially wrongful removal of an adrenal, it is also possible that such inaccuracies may impact the diagnostic process and treatment for other patients

Head/neck paragangliomas: focus on tumor location, mutational status and plasma methoxytyramine

Head and neck paragangliomas (HNPGLs) are tumors of parasympathetic origin that occur at variable locations and are often secondary to germline mutations in succinate dehydrogenase (SDH) subunit genes. Occasionally, these tumors produce catecholamines. Here, we assessed whether different locations of HNPGLs relate to the presence of SDHx mutations, catecholamine production and other presentations. In this multicenter study, we collected clinical and biochemical data from 244 patients with HNPGLs and 71 patients without HNPGLs. We clarified that jugulotympanic HNPGLs have distinct features. In particular, 88% of jugulotympanic HNPGLs arose in women, among whom only 24% occurred due to SDHx mutations compared to 55% in men. Jugulotympanic HNPGLs were also rarely bilateral, were of a smaller size and were less often metastatic compared to carotid body and vagal HNPGLs. Furthermore, we showed that plasma concentrations of methoxytyramine (MTY) were higher (P < 0.0001) in patients with HNPGL than without HNPGL, whereas plasma normetanephrine did not differ. Only 3.7% of patients showed strong increases in plasma normetanephrine. Plasma MTY was positively related to tumor size but did not relate to the presence of SDHx mutations or tumor location. Our findings confirm that increases in plasma MTY represent the main catecholamine-related biochemical feature of patients with HNPGLs. We expect that more sensitive analytical methods will make biochemical testing of HNPGLs more practical in the future and enable more than the current 30% of patients to be identified with dopamine-producing HNPGLs. The sex-dependent differences in the development of HNPGLs may have relevance to the diagnosis, management and outcomes of these tumors. Keywords: biochemical phenotype; methoxytyramine; normetanephrine; sex-related differences; succinate dehydrogenase mutations; tumor size