Data record for the article: Deep learning for diagnosis of Acute Promyelocytic Leukemia via recognition of genomically imprinted morphologic features

This metadata record provides details of the data supporting the claims of the related article: “Deep learning for diagnosis of Acute Promyelocytic Leukemia via recognition of genomically imprinted morphologic features”. The related study aimed to demonstrate a deep learning method to assist with the diagnosis of Acute Promyelocytic Leukemia (APL), which is a subtype of Acute Myeloid Leukemia (AML). Deep learning pattern recognition was applied to the peripheral blood smear haematopathology images, which are usually taken at clinical presentation. Type of data: Images of peripheral blood smear results from AML and APL patients; Metadata table for the images Subject of data: Retrospectively identified APL and AML patients presenting at Johns Hopkins Hospital Sample size: 106 study participants Population characteristics: APL patients were separated into a discovery cohort presenting prior to 1/2019 (n = 22) and a validation cohort presenting on or after 1/2019 (n = 12). AML patients were separated into a discovery cohort presenting prior to 1/2019 (n = 60) and a validation cohort presenting on or after 1/2019 (n = 12). Recruitment: Patients with APL were identified via retrospective chart review from a list of confirmed FISH t(15;17)-positive patients presenting at The Johns Hopkins Hospital (JHH) who met the inclusion criteria (n = 34) of presentation at the time of initial diagnosis, without history of remission, presentation prior to treatment initiation, and availability of peripheral blood smear image. Patients with AML were identified via retrospective chart review from a list of patients presenting to JHH who at initial presentation had a bone marrow biopsy showing >20% blasts and by acquiring a query of patients who tested negative for the t(15;17) translocation by FISH and who were then confirmed to have AML by bone marrow biopsy and other genetic studies. Geographic location: United States of America Data access The peripheral smear images from 106 de-identified study participants are openly available as part of this figshare metadata record. All images are in jpg format. Within the ‘blood smear images_Patient00-105.zip’ file, the images are arranged into folders named ‘Patient_00’ to ‘Patient_105’. Within each participant folder images are divided into subfolders named ‘Unsigned slides’ and ‘Signed slides’. The signed folder contains the images organized by Cellavision cell type, and the unsigned folder contains the images not subdivided by Cella