scholarly journals Circulating tumour cells: a broad perspective

2020 ◽  
Vol 17 (168) ◽  
pp. 20200065 ◽  
Author(s):  
Victor Akpe ◽  
Tak H. Kim ◽  
Christopher L. Brown ◽  
Ian E. Cock
Keyword(s):  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Tumour Cells ◽  
Detection Methods ◽  
Circulating Tumour Cells ◽  
Intrinsic Properties ◽  
Broad Perspective ◽  
Cellular Invasion ◽  
Rare Cells

Circulating tumour cells (CTCs) have recently been identified as valuable biomarkers for diagnostic and prognostic evaluations, as well for monitoring therapeutic responses to treatments. CTCs are rare cells which may be present as one CTC surrounded by approximately 1 million white blood cells and 1 billion red blood cells per millilitre of peripheral blood. Despite the various challenges in CTC detection, considerable progress in detection methods have been documented in recent times, particularly for methodologies incorporating nanomaterial-based platforms and/or integrated microfluidics. Herein, we summarize the importance of CTCs as biological markers for tumour detection, highlight their mechanism of cellular invasion and discuss the various challenges associated with CTC research, including vulnerability, heterogeneity, phenotypicity and size differences. In addition, we describe nanomaterial agents used for electrochemistry and surface plasmon resonance applications, which have recently been used to selectively capture cancer cells and amplify signals for CTC detection. The intrinsic properties of nanomaterials have also recently been exploited to achieve photothermal destruction of cancer cells. This review describes recent advancements and future perspectives in the CTC field.

scholarly journals Detection of Blood Cancer Cells Using Microscopic Images

2021 ◽  
Vol 9 (8) ◽  
pp. 995-1003
Author(s):  
Vidyashree M S
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Blood Cancer ◽  
Acute Myeloid

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

Segmentation of Leukemia Cells Using Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 39-48
Author(s):  
Eman Mostafa ◽  
Heba A. Tag El-Dien
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Microscopic Image ◽  
Processing Stage ◽  
Clustering Techniques ◽  
Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

scholarly journals Diagnosis, Response Assessment and Treatment Outcomes in Acute Myeloid Leukemia (AML)

2021 ◽  
pp. 665-705
Author(s):  
Alireza Heidari ◽  
Ricardo Gobato ◽  
Abhijit Mitra
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
Prolonged Exposure ◽  
Genetic Diseases ◽  
White Blood Cells ◽  
Response Assessment ◽  
Severe Bleeding ◽  
Proliferating Cells ◽  
Assessment And Treatment

Leukemia occurs when a person's entire bone marrow tissue space is occupied by cancer cells or blasts that are young, dysfunctional, undifferentiated, and proliferating cells. In this situation, there is no space left for the bone marrow to be able to produce normal blood cells such as platelets, red and white blood cells. These patients suffer from severe bleeding due to decreased platelets or due to a decrease in white blood cells, which are often diagnosed with dangerous infections that cause death in these patients. The exact cause of leukemias is not yet known, but a number of factors have been identified that play a role in the development of these cancers, including high doses of radiation or atomic radiation, prolonged exposure to certain chemicals, and some Mentioned viruses and some genetic diseases such as Down syndrome or underlying diseases. Keywords: Cancer; Cells; Tissues; Tumors; Prevention; Prognosis; Diagnosis; Imaging; Screening, Treatment; Management

scholarly journals Discrimination between Breast Cancer Cells and White Blood Cells by Non-Invasive Measurements: Implications for a Novel In Vitro-Based Circulating Tumor Cell Model Using Digital Holographic Cytometry

2020 ◽  
Vol 10 (14) ◽  
pp. 4854
Author(s):  
Zahra El-Schich ◽  
Birgit Janicke ◽  
Kersti Alm ◽  
Nishtman Dizeyi ◽  
Jenny L. Persson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Cell ◽  
Cancer Cells ◽  
Cell Lines ◽  
Blood Cells ◽  
Breast Cancer Cells ◽  
Cell Model ◽  
White Blood Cells ◽  
Circulating Tumor Cell

Breast cancer is the second most common cancer worldwide. Metastasis is the main reason for death in breast cancer, and today, there is a lack of methods to detect and isolate circulating tumor cells (CTCs), mainly due to their heterogeneity and rarity. There are some systems that are designed to detect rare epithelial cancer cells in whole blood based on the most common marker used today, the epithelial cell adhesion molecule (EpCAM). It has been shown that aggressive breast cancer metastases are of non-epithelial origin and are therefore not always detected using EpCAM as a marker. In the present study, we used an in vitro-based circulating tumor cell model comprising a collection of six breast cancer cell lines and white blood cell lines. We used digital holographic cytometry (DHC) to characterize and distinguish between the different cell types by area, volume and thickness. Here, we present significant differences in cell size-related parameters observed when comparing white blood cells and breast cancer cells by using DHC. In conclusion, DHC can be a powerful diagnostic tool for the characterization of CTCs in the blood.

scholarly journals Biocompatible and label-free separation of cancer cells from cell culture lines from white blood cells in ferrofluids

Lab on a Chip ◽  
2017 ◽  
Vol 17 (13) ◽  
pp. 2243-2255 ◽  
Author(s):  
Wujun Zhao ◽  
Rui Cheng ◽  
So Hyun Lim ◽  
Joshua R. Miller ◽  
Weizhong Zhang ◽  
...  
Keyword(s):  
Cell Culture ◽  
Cancer Cells ◽  
Cell Lines ◽  
Blood Cells ◽  
White Blood Cells ◽  
Label Free ◽  
Low Concentration

A biocompatible and label-free method for separation of low-concentration cancer cells from cell lines from white blood cells is developed.

scholarly journals Dynamic blood flow phantom for in vivo liquid biopsy standardization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anastasiia Kozlova ◽  
Daniil Bratashov ◽  
Oleg Grishin ◽  
Arkadii Abdurashitov ◽  
Ekaterina Prikhozhdenko ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Blood Cells ◽  
Liquid Biopsy ◽  
High Speed ◽  
White Blood Cells ◽  
High Sensitivity ◽  
Acoustic Properties ◽  
Detection Methods ◽  
In Vivo Flow Cytometry

AbstractIn vivo liquid biopsy, especially using the photoacoustic (PA) method, demonstrated high clinical potential for early diagnosis of deadly diseases such as cancer, infections, and cardiovascular disorders through the detection of rare circulating tumor cells (CTCs), bacteria, and clots in the blood background. However, little progress has been made in terms of standardization of these techniques, which is crucial to validate their high sensitivity, accuracy, and reproducibility. In the present study, we addressed this important demand by introducing a dynamic blood vessel phantom with flowing mimic normal and abnormal cells. The light transparent silica microspheres were used as white blood cells and platelets phantoms, while hollow polymeric capsules, filled with hemoglobin and melanin, reproduced red blood cells and melanoma CTCs, respectively. These phantoms were successfully used for calibration of the PA flow cytometry platform with high-speed signal processing. The results suggest that these dynamic cell flow phantoms with appropriate biochemical, optical, thermal, and acoustic properties can be promising for the establishment of standardization tool for calibration of PA, fluorescent, Raman, and other detection methods of in vivo flow cytometry and liquid biopsy.

scholarly journals Analisis dan Strategi Pengendalian Model Matematika Interaksi Sel Kanker Leukemia Mielositik Kronis dan Sel Imunitas

2020 ◽  
Vol 2 (2) ◽  
pp. 89
Author(s):  
Nanda Amalia Rahma ◽  
Cicik Alfiniyah ◽  
Windarto Windarto
Keyword(s):  
Equilibrium Point ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Immune Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Populations ◽  
Asymptotically Stable ◽  
Chronic Myelocytic Leukemia ◽  
Myelocytic Leukemia

Leukemia is a disease in the classification of cancer in the blood that is characterized by abnormal growth of blood cells in the bone marrow or lymphoid tissue, and generally occurs in leukocytes or white blood cells. White blood cells that look for types of pathogenic diseases that harm the human body and then damage it are the task of the immune system. This thesis analyzes the mathematical model of chronic myelocytic leukemia cancer cell interactions and immune cells to determine the rate of increase in the population of chronic myelocytic leukemia cancer cells to the effect of immune cells. Based on the analysis of the model obtained two equilibrium points namely the equilibrium point of the extinction of chronic myelocytic leukemia cancer cells (E0) and the equilibrium point of the coexistence of chronic myelocytic leukemia cancer cells (E1). The equilibrium point of extinction will be asymptotically stable, whereas the equilibrium point of coexistence tends to be asymptotically stable using phase fields with the help of MATLAB software. Numerical simulation results show that there is an increase in the number of chronic myelocytic leukemia cancer cell populations and a decrease in the number of vulnerable blood cell populations. When immune cells increase in population, chronic myelocytic leukemia in cancer cells decreases in population but is not significant.

scholarly journals A Comparison of Flow Cytometry, Bone Marrow Biopsy and Bone Marrow Aspirates in the Detection of Lymphoid Infiltration in T Cell Disorders

2021 ◽  
pp. 674-714
Author(s):  
Elena Locci ◽  
Silvia Raymond
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
Prolonged Exposure ◽  
Genetic Diseases ◽  
White Blood Cells ◽  
Severe Bleeding ◽  
Proliferating Cells ◽  
High Doses ◽  
Lymphoid Infiltration

Leukemia occurs when a person's entire bone marrow tissue space is occupied by cancer cells or blasts that are young, dysfunctional, undifferentiated, and proliferating cells. In this situation, there is no space left for the bone marrow to be able to produce normal blood cells such as platelets, red and white blood cells. These patients suffer from severe bleeding due to decreased platelets or due to a decrease in white blood cells, which are often diagnosed with dangerous infections that cause death in these patients. The exact cause of leukemias is not yet known, but a number of factors have been identified that play a role in the development of these cancers, including high doses of radiation or atomic radiation, prolonged exposure to certain chemicals, and some Mentioned viruses and some genetic diseases such as Down syndrome or underlying diseases. Keywords: Cancer; Cells; Tissues, Tumors; Prevention, Prognosis; Diagnosis; Imaging; Screening; Treatment; Management

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