Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation

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.

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis

“…Moreover, a DM analysis involving 28,236 registered adult HSCT receiving patients from the European The ADTree algorithm was employed to create models using 70% of the data set, and the remaining 30% of the data was utilized to validate them [41]. Moreover, Arabyarmohammadi et al used the Cox regression model to estimate the probability of patient relapse after acute myeloid leukemia posthematopoietic cell transplantation [42]. Similarly, Iwasaki et al created a stacked ensemble of the Cox proportional hazard (Cox-PH) regression and 7 machine learning algorithms and discovered prediction accuracy with a C -index of 0.670 utilizing the ensemble model [43].…”

“…The sole purpose of this research is to investigate whether HPO along with a reduced feature set can provide a reliable outcome using an investigative ML approach and to distinguish the most impactful factors on children's survival who have received BMTs. A preprint has previously been published in [42]. California, Irvine, and the version utilized in this study was extracted from [27].…”

MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology

“…1,[3][4][5][6][7] In hematopathology, recent advances in digital and quantitative pathology have renewed interest in developing faster and more quantitative assessments of bone marrow (BM) trephine biopsies, which are a key component in the diagnosis and followup of hematologic and nonhematologic disorders. [8][9][10][11] BM biopsy assessment informs on tissue architecture, such as cellularity, necrosis, inflammation, cell lineages, metastatic spread, and stromal modifications, 12 with cellularity being a key factor reflecting hematopoietic function. For instance, low cellularity highlights a central defect in blood production (toxic, constitutional, or idiopathic), whereas high cellularity can suggest a neoplastic transformation.…”

MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology

“…In the context of hematopathology, recent advances in digital and quantitative pathology have renewed interest in developing faster and more quantitative assessments of bone marrow (BM) trephine biopsies, which are a key component in the diagnosis and follow-up of hematological and non-hematological disorders (8)(9)(10)(11). BM biopsy assessment informs on tissue architecture, including the cellularity, necrosis, in ammation, cell lineages, metastatic spread, and stromal modi cations (12), with cellularity being a key factor re ecting hematopoietic function.…”