{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T15:58:53Z","timestamp":1787932733327,"version":"build-2784847793"},"reference-count":44,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"6","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p> 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. <\/jats:p><\/jats:sec><jats:sec><jats:title>MATERIALS AND METHODS<\/jats:title><jats:p> In this study, Wright-Giemsa\u2013stained post-HCT aspirate images were collected from 92 patients with AML\/MDS who were randomly assigned into a training set ( S<jats:sub> t<\/jats:sub> = 52) and a validation set ( S<jats:sub> v<\/jats:sub> = 40). First, a deep learning\u2013based 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. <\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p> The risk score was associated with RFS in S<jats:sub> t<\/jats:sub> (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and S<jats:sub> v<\/jats:sub> (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<jats:sub> v<\/jats:sub>. All the relevant code is available at GitHub. <\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p> The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML\/MDS. <\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.21.00156","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T19:59:29Z","timestamp":1651867169000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation"],"prefix":"10.1200","author":[{"given":"Sara","family":"Arabyarmohammadi","sequence":"first","affiliation":[{"name":"Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, 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