Project C12

ProMapp: Prospectiveclinical trial utilizing machine learning for more efficient diagnosis and stratification of pheochromocytoma and paraganglioma


Christina Pamporaki, MD, Ph.D
christina.pamporaki(at)uniklinikum-dresden.de
Medical Clinic III
University Hospital Carl Gustav Carus Dresden

Prof. Dr. Graeme Eisenhofer
graeme.eisenhofer(at)uniklinikum-dresden.de
Institut of Clinical Chemistry and Laboratory Medicine
University Hospital Carl Gustav Carus Dresden

Prof. Jacques Lenders, MD, PhD
jacques.lenders(at)radboudumc.nl
Medical Clinic III
University Hospital Carl Gustav Carus Dresden

Scientific Staff:

Angelos Filippatos – Groupleader

Georg Pommer  – Student

Manuel Schulze-TA

Carola Kunath – TA

Project description:

We have recently established machine learning (ML) models for streamlining diagnostic stratification of patients with suspected pheochromocytomas/paragangliomas (PGGLs) and further identification of those with synchronous metastatic disease or at high risk for developing metastases. Promapp is an international multicenter prospective cohort study with randomized intervention that will actually establish whether our ML models can be used to more efficiently and effectively to streamline the processes for both diagnostic screening of patients with suspected PPGL and follow-up of confirmed cases for identifying presence of or future development of metastatic disease.

Aims

(I) Establish whether provision of machine learning-based interpretations to standard laboratory results (i.e., plasma metanephrines and methoxytyramine) improves the efficiency and effectiveness of confirmation or exclusion of PPGL (Phase 1).

(II) Establish whether provision of machine learning-based interpretations to standard routine care improves the efficiency and effectiveness of clinicians for diagnosis of metastatic disease (Phase 2).

(III) To prospectively validate and improve ML algorithm-based models through learning-based adjustments under real time diagnostic conditions in a population based diagnostic approach where the prevalence of a PPGL is expected at around 1% and the prevalence of metastatic disease for patients with PPGL at around 15-20%.

Publications:

1. Tischler AS. Pheochromocytoma and extra-adrenal paraganglioma: updates. Arch Pathol

2. Eisenhofer G, Lenders JW, Siegert G, et al. Plasma methoxytyramine: a novel biomarker of metastatic pheochromocytoma and paraganglioma in relation to established risk factors of tumour size, location and SDHB mutation status. Eur J Cancer. 2012;48(11):1739-49.

3. Lam AK. Update on Adrenal Tumours in 2017 World Health Organization (WHO) of Endocrine Tumours. Endocr Pathol. 2017;28(3):213-227.

4. Fassnacht M, Assie G, Baudin E, et al. Adrenocortical carcinomas and malignant phaeochromocytomas: ESMO-EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2020;31(11):1476-1490.

5. Plouin PF, Amar L, Dekkers OM, et al. European Society of Endocrinology Clinical Practice Guideline for long-term follow-up of patients operated on for a phaeochromocytoma or a paraganglioma. Eur J Endocrinol. 2016;174(5):G1-G10.

6. Pamporaki C, Prodanov T, Meuter L, et al. Determinants of disease-specific survival in patients with and without metastatic pheochromocytoma and paraganglioma. Eur J Cancer. 2022;169:32-41.

7. Pamporaki C, Hamplova B, Peitzsch M, et al. Characteristics of Pediatric vs Adult Pheochromocytomas and Paragangliomas. J Clin Endocrinol Metab. 2017;102(4):1122-1132..

8. Monteagudo M, Martínez P, Leandro-García LJ, et al. Analysis of Telomere Maintenance Related Genes Reveals NOP10 as a New Metastatic-Risk Marker in Pheochromocytoma/Paraganglioma. Cancers (Basel). 2021;13(19):4758.

9. Mcmillan, M. Identification of hydroxytyramine in a chromaffin tumour. Lancet 1956; 271(6937):284.

10. van der Harst E, de Herder WW, de Krijger RR, et al. The value of plasma markers for the clinical behaviour of phaeochromocytomas. Eur J Endocrinol. 2002;147(1):85-94.

11. Rao D, Peitzsch M, Prejbisz A, et al. Plasma methoxytyramine: clinical utility with metanephrines for diagnosis of pheochromocytoma and paraganglioma. Eur J Endocrinol. 2017;177(2):103-113.

12. Obermeyer Z, Lee TH. Lost in Thought – The Limits of the Human Mind and the Future of Medicine. N Engl J Med. 2017;377(13):1209-1211. PMID: 28953443; PMCID: PMC5754014.

13. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020;155(2):148-158.

14. Seymour CW, Kennedy JN, Wang S, et al. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA. 2019;321(20):2003-2017.

15. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology – new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-715.

16. Eisenhofer G, Deutschbein T, Constantinescu G, et al. Plasma metanephrines and prospective prediction of tumor location, size and mutation type in patients with pheochromocytoma and paraganglioma. Clin Chem Lab Med. 2020;59(2):353-363.

17. Lenders JW, Eisenhofer G, Armando I, Keiser HR, Goldstein DS, Kopin IJ. Determination of metanephrines in plasma by liquid chromatography with electrochemical detection. Clin Chem. 1993;39(1):97–103.

18. Peitzsch M, Prejbisz A, Kroiß M, et al. Analysis of plasma 3-methoxytyramine, normetanephrine and metanephrine by ultraperformance liquid chromatography-tandem mass spectrometry: utility for diagnosis of dopamine-producing metastatic phaeochromocytoma. Ann Clin Biochem. 2013;50(Pt 2):147–155.

19. Goffredo P, Sosa JA, Roman SA. Malignant pheochromocytoma and paraganglioma: a population level analysis of long-term survival over two decades. J Surg Oncol 2013;107(6):659–66.

20. Amar L, Baudin E, Burnichon N, et al. Succinate dehydrogenase B gene mutations predict survival in patients with malignant pheochromocytomas or paragangliomas. J Clin Endocrinol Metab 2007;92(10):3822-8.

21. Qin N, de Cubas AA, Garcia-Martin R, et al. Opposing effects of HIF1α and HIF2α on chromaffin cell phenotypic features and tumor cell proliferation: Insights from MYC-associated factor X. Int J Cancer 2014;135(9):2054-64.

22. Letouzé E, Martinelli C, Loriot C, et al. SDH mutations establish a hypermethylator phenotype in paraganglioma. Cancer Cell. 2013;23(6):739-52

23. Thienpont B, Steinbacher J, Zhao H, et al. Tumour hypoxia causes DNA hypermethylation by reducing TET activity. Nature. 2016;537(7618):63-68.

24. Morin A, Goncalves J, Moog S, et al. TET-Mediated Hypermethylation Primes SDH-Deficient Cells for HIF2α-Driven Mesenchymal Transition. Cell Rep. 2020;30(13):4551-4566.e7.

25. Eisenhofer G, Prejbisz A, Peitzsch M, et al. Biochemical Diagnosis of Chromaffin Cell Tumors in Patients at High and Low Risk of Disease: Plasma versus Urinary Free or Deconjugated O-Methylated Catecholamine Metabolites. Clin Chem. 2018;64(11):1646-1656. 

26. Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020;111(5):1452-1460.

27. Wang X, Wang D, Yao Z, Xin B, Wang B, Lan C, Qin Y, Xu S, He D, Liu Y. Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations. Front Neurosci. 2019;12:1046.

28. Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, Tang PC. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527-34.

29. Jia P, Zhang L, Chen J, Zhao P, Zhang M. The Effects of Clinical Decision Support Systems on Medication Safety: An Overview. PLoS One. 2016;11(12):e0167683.