Timo M Deist1, Frank J W M Dankers2, Priyanka Ojha3, M Scott Marshall3, Tomas Janssen3, Corinne Faivre-Finn4, Carlotta Masciocchi5, Vincenzo Valentini6, Jiazhou Wang7, Jiayan Chen7, Zhen Zhang7, Emiliano Spezi8, Mick Button9, Joost Jan Nuyttens10, René Vernhout10, Johan van Soest11, Arthur Jochems12, René Monshouwer13, Johan Bussink13, Gareth Price4, Philippe Lambin12, Andre Dekker14. 1. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands; The D-Lab: Dpt of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. 2. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands; Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands. 3. Department of Radiation Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands. 4. The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom. 5. Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. 6. Università Cattolica del Sacro Cuore, Rome, Italy; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. 7. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. 8. School of Engineering, Cardiff University, United Kingdom; Velindre Cancer Centre, Cardiff, United Kingdom. 9. Velindre Cancer Centre, Cardiff, United Kingdom. 10. Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 11. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. 12. The D-Lab: Dpt of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. 13. Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands. 14. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. Electronic address: andre.dekker@maastro.nl.
Abstract
BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. MATERIALS AND METHODS: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. RESULTS: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. CONCLUSION: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.
BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. MATERIALS AND METHODS:Lung cancerpatient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. RESULTS: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. CONCLUSION: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.
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