Investigation of Biomarkers Associated with Low Platelet Counts in Normal Karyotype Acute Myeloid Leukemia

Park, Chang-Hun; Yun, Jae Won

doi:10.3390/ijms23147772

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…This factor has the effect of reducing the computation and size of the model. MobileNet architecture contains 28 layers (depth-wise and point-wise layers) followed by ReLU and batchnorm [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

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Section: Methodsmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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“…An original article published in this Special Issue identified three biomarkers (CSF1R, TNFSF15, and CLEC10A) whose expression was associated with reduced platelet levels in normal karyotype AML. These biomarkers could potentially help predict which AML patients are at an increased risk of thrombocytopenia [51].…”

Section: Antiapoptotic Agents In Amlmentioning

confidence: 99%

Biomarkers of Response to Venetoclax Therapy in Acute Myeloid Leukemia

Rodríguez-Medina,

Stuckey,

Bilbao-Sieyro

et al. 2024

IJMS

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Recent progress in the use of massive sequencing technologies has greatly enhanced our understanding of acute myeloid leukemia (AML) pathology. This knowledge has in turn driven the development of targeted therapies, such as venetoclax, a BCL-2 inhibitor approved for use in combination with azacitidine, decitabine, or low-dose cytarabine for the treatment of newly diagnosed adult patients with AML who are not eligible for intensive chemotherapy. However, a significant number of AML patients still face the challenge of disease relapse. In this review, we will explore biomarkers that may predict disease progression in patients receiving venetoclax-based therapy, considering both clinical factors and genetic changes. Despite the many advances, we conclude that the identification of molecular profiles for AML patients who will respond optimally to venetoclax therapy remains an unmet clinical need.

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