Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation. (6th May 2022)

• Record Type: Journal Article
• Authors: Arabyarmohammadi, Sara; Leo, Patrick; Viswanathan, Vidya Sankar; Janowczyk, Andrew; Corredor, German; Fu, Pingfu; Meyerson, Howard; Metheny, Leland; Madabhushi, Anant
• Is Part Of: JCO Clinical Cancer Informatics. Volume 6(2022)
• Publication Date: 2022-05-06
• Subjects: 616.994
• DOI: 10.1200/CCI.21.00156
• Languages: English
• ISSNs: 2473-4276

Abstract

Abstract : PURPOSE: Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT. MATERIALS AND METHODS: In this study, Wright-Giemsa–stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set ( S t = 52) and a validation set ( S v = 40). First, a deep learning–based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model. RESULTS: The risk score was associated with RFS in S t (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and S v (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within S v . All the relevant code is available at GitHub. CONCLUSION: The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS. Abstract :