The successful non-invasive management of pulmonary thromboembolism in a child with acute lymphoblastic leukemia

AbstractDysregulation of non-coding microRNAs during the course of tumor development, invasion and/or progression to the distant organs, makes them a promising candidate marker for the diagnosis of cancer and associated malignancies. This exploratory study aims at evaluating the usefulness of plasma concentration of circulating mir-146a as a non-invasive biomarker for acute lymphoblastic leukemia (ALL). Total RNA including miRNA was isolated from 110 plasma samples of patients (n = 66), healthy controls (n = 24) and follow up (n = 20) cases and reverse transcribed. Relative concentrations were assessed using real-time quantitative PCR and fold-change was calculated by 2−ΔΔCt method. Finally, relative concentrations were correlated to clinicopathological factors. Patients (n = 66) were analyzed to determine fold expression of miR-146a in plasma samples of ALL. Before chemotherapy, pediatric (n = 42) and adult (n = 24) showed overexpression of miR-146a compared with healthy controls (P < 0.0001). There was no effect of age and gender on mir-146a expression in plasma. mirR-146a expression was independent of clinical and hematological features. Moreover, miR-146a levels in plasma of paired samples (n = 20) after treatment showed significant decrease in expression (P < 0.001). Expression of plasma miR-146a may be utilized as non-invasive marker to diagnose and predict prognosis in pediatric and adult patients with ALL. Moreover predicted targets may be utilized for ALL therapy in future.

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An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

Applied Sciences ◽

10.3390/app112210662 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10662

Author(s):

Muhammad Zakir Ullah ◽

Yuanjie Zheng ◽

Jingqi Song ◽

Sehrish Aslam ◽

Chenxi Xu ◽

...

Keyword(s):

Neural Network ◽

Bone Marrow ◽

Acute Lymphoblastic Leukemia ◽

Convolutional Neural Network ◽

Lymphoblastic Leukemia ◽

Diagnostic Techniques ◽

Feature Representation ◽

Training Data ◽

Non Invasive ◽

Augmentation Techniques

Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.

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