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*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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 post-transplant 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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510634077
StatePublished - 2020
EventMedical Imaging 2020: Digital Pathology - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2020: Digital Pathology
Country/TerritoryUnited States


  • Acute myeloid leukemia
  • Deep learning
  • Myeloblasts
  • Relapse prediction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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