Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology

Author:

D'Amico Saverio1ORCID,Dall’Olio Daniele2ORCID,Sala Claudia3ORCID,Dall’Olio Lorenzo2ORCID,Sauta Elisabetta1ORCID,Zampini Matteo1ORCID,Asti Gianluca1ORCID,Lanino Luca14ORCID,Maggioni Giulia14,Campagna Alessia1,Ubezio Marta1,Russo Antonio1ORCID,Bicchieri Maria Elena1ORCID,Riva Elena1ORCID,Tentori Cristina A.14,Travaglino Erica4,Morandini Pierandrea1ORCID,Savevski Victor1,Santoro Armando14ORCID,Prada-Luengo Iñigo5,Krogh Anders5ORCID,Santini Valeria6ORCID,Kordasti Shahram78ORCID,Platzbecker Uwe9ORCID,Diez-Campelo Maria10,Fenaux Pierre11,Haferlach Torsten12,Castellani Gastone23ORCID,Della Porta Matteo Giovanni14ORCID

Affiliation:

1. IRCCS Humanitas Research Hospital, Milan, Italy

2. Department of Physics and Astronomy (DIFA), Bologna, Italy

3. Experimental, Diagnostic and Specialty Medicine—DIMES, Bologna, Italy

4. Department of Biomedical Sciences, Humanitas University, Milan, Italy

5. Department of Computer Science & Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark

6. Hematology, Azienda Ospedaliero-Universitaria Careggi & University of Florence, Florence, Italy

7. Hematology, Guy's Hospital & Comprehensive Cancer Centre, King's College, London, United Kingdom

8. Hematology Department & Stem Cell Transplant Unit, DISCLIMO-Università Politecnica delle Marche, Ancona, Italy

9. Medical Clinic and Policlinic 1, Hematology and Cellular Therapy, University Hospital Leipzig, Leipzig, Germany

10. Hematology Department, Hospital Universitario de Salamanca, Salamanca, Spain

11. Hematology and Bone Marrow Transplantation, Hôpital Saint-Louis/University Paris 7, Paris, France

12. MLL Munich Leukemia Laboratory, Munich, Germany

Abstract

PURPOSE Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology. METHODS A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data. RESULTS We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients. CONCLUSION Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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