Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Sara ArabYarmohammadi; Zelin Zhang; Patrick Leo; Marjan Firouznia; Andrew Janowczyk; Haojia Li; Nathaniel M. Braman; Kaustav Bera; Behtash Nezami; Howard Meyerson; Jun Xu; Leland Metheny; Anant Madabhushi

doi:10.1117/12.2549730

16 March 2020 Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Sara ArabYarmohammadi, Zelin Zhang, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel M. Braman, Kaustav Bera, Behtash Nezami, Howard Meyerson, Jun Xu, Leland Metheny, Anant Madabhushi

Author Affiliations +

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 1132004 (2020) https://doi.org/10.1117/12.2549730
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Allogenic hematopoietic stem cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML). Relapse after HCT is the most common cause of treatment failure and is associated with poor prognosis. Early identification of which patients are at elevated risk of relapse may justify use of aggressive post-HCT treatment options, potentially preventing relapse and treatment failure. In this study, our goal was to predict relapse after HCT in AML patients using quantitative features extracted from digitized Wright-Giemsa stained posttransplant aspirate smears. We collected 39 aspirate specimens from a cohort of 39 AML patients after HCT, of which 25 experienced relapse, while 14 did not. Our approach comprised the following main steps. First, a deep learning model was developed to segment myeloblasts, a cell type in bone marrow that accumulates and characterizes AML. A total of 161 texture and shape descriptors were then extracted from these segmented myeloblasts. The top eight predictive features were identified using a Wilcoxon rank sum test over 100 iterations of 3-fold cross validation. A model was subsequently built employing these features and yielded an average area under the receiver operating characteristic curve of 0.80±0.05 in cross validation. The top eight features include four Haralick texture features and four fractal dimension features. The texture features appear to characterize chromatin patterns in myeloblasts while the fractal features quantify morphological irregularity and complexity of myeloblasts, in alignment with findings previously reported for AML patients post-treatment.

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Sara ArabYarmohammadi, Zelin Zhang, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel M. Braman, Kaustav Bera, Behtash Nezami, Howard Meyerson, Jun Xu, Leland Metheny, and Anant Madabhushi "Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132004 (16 March 2020); https://doi.org/10.1117/12.2549730

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