期刊
JCO CLINICAL CANCER INFORMATICS
卷 6, 期 1, 页码 -出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1200/CCI.21.00156
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资金
- National Cancer Institute [R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01]
- National Heart, Lung, and Blood Institute [1R01HL15127701A1, R01HL15807101A1]
- National Institute of Biomedical Imaging and Bioengineering [1R43EB028736-01]
- National Center for Research Resources [1 C06 RR12463-01]
- United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program [IBX004121A, W81XWH-19-1-0668]
- Prostate Cancer Research Program [W81XWH-15-1-0558, W81XWH-20-1 0851]
- Lung Cancer Research Program [W81XWH-18-1-0440, W81XWH-20-1-0595]
- Peer-Reviewed Cancer Research Program [W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160]
- Kidney Precision Medicine Project (KPMP) Glue Grant
- Bristol Myers-Squibb
- Boehringer-Ingelheim
- Eli-Lilly
- AstraZeneca
Computer-extracted morphology and texture features from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS in patients with AML/MDS.
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 AM L 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. (C) 2022 by American Society of Clinical Oncology
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