ABSTRACT Background Medullary thyroid cancer (MTC) is a neuroendocrine tumor comprising approximately 1%–2% of all thyroid malignancies. The rarity and more aggressive biology of MTC requires robust sample sizes to enhance our understanding of this complex disease. Harmonization is the process of standardizing raw data from multiple sources, by resolving differences in format and terminology, to create a unified dataset that can be analyzed for a common purpose. The aim of this project was to assess the feasibility of collaboration, data mapping, and harmonization among clinical sites investigating MTC internationally. Methods The Maelstrom guidelines were used to perform retrospective data harmonization from three clinical networks in Australia and the Unites States for adult patients with MTC, between 2018 and 2021. Data received were categorized as an exact, close, or low match. Exact and close matches were combined to form a harmonized dataset. A logistic regression analysis was then performed to determine pre‐operative factors associated with the presence of cervical lymph node metastases. Results Data were received from three separate clinical networks. This comprised 114 patients, 17 hospitals and 4674 data points. The completeness of data received ranged from 57.4% to 97.3%. Overall, 80.8% of data received were suitable for harmonization including basic demographics, basis of diagnosis, genetic testing (but not results), select clinical findings, pre‐operative investigations, operative details, histopathology, and TNM staging. The prevalence of palpable lymph node involvement at presentation in the harmonized dataset was 15.8%. Younger patients (less than 55 years) and patients with abnormal nodes on ultrasound were strongly associated with cervical lymph node metastases. Conversely, patients with an incidental diagnosis of MTC had markedly lower odds of presenting with cervical lymph node metastases. Conclusion Data mapping and harmonization across national and international sites is feasible and enables meaningful modeling that would not be possible with individual datasets. The Maelstrom guidelines provide a useful template regarding how to achieve this efficiently. This manuscript is a white paper for clinicians and researchers studying rare diseases, such as MTC, regarding how to share heterogeneous raw data and collaborate with other clinical sites.
Moore et al. (Thu,) studied this question.