Classification of cancer cells using computational analysis of dynamic morphology

Hasan, Md. Mehedi; Hassan, Naeemul; Khan, Rayan; Kim, Young-Tae; Iqbal, Samir M.

doi:10.1016/j.cmpb.2017.12.003

Cited by 29 publications

(22 citation statements)

References 48 publications

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“…In fact, one year later, the same group designed a flow-through lab-on-chip device that took advantage of the surface-bound aptamer’s affinity for EGFR, the biomarker overexpressed in GBM, to demonstrate that a microfluidic-based approach can be used to detect and isolate GBM cells [ 31 ]. The group then advanced the diagnostic use of anti-EGFR aptamers with two subsequent articles on the tracking of the differential dynamics of GBM cell morphology on aptamer-grafted substrates, and on the analysis of dynamic morphology in computational single-cell metrics to detect and recognize tumor cells [ 22 , 29 ].…”

Section: Resultsmentioning

confidence: 99%

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

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et al. 2020

Cancers

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Glioblastoma (GBM) is the most lethal primary brain tumor of the central nervous system in adults. Despite advances in surgical and medical neuro-oncology, the median survival is about 15 months. For this reason, initial diagnosis, prognosis, and targeted therapy of GBM represent very attractive areas of study. Aptamers are short three-dimensional structures of single-stranded nucleic acids (RNA or DNA), identified by an in vitro process, named systematic evolution of ligands by exponential enrichment (SELEX), starting from a partially random oligonucleotide library. They bind to a molecular target with high affinity and specificity and can be easily modified to optimize binding affinity and selectivity. Thanks to their properties (low immunogenicity and toxicity, long stability, and low production variability), a large number of aptamers have been selected against GBM biomarkers and provide specific imaging agents and therapeutics to improve the diagnosis and treatment of GBM. However, the use of aptamers in GBM diagnosis and treatment still represents an underdeveloped topic, mainly due to limited literature in the research world. On these bases, we performed a systematic review aimed at summarizing current knowledge on the new promising DNA and RNA aptamer-based molecules for GBM diagnosis and treatment. Thirty-eight studies from 2000 were included and investigated. Seventeen involved the use of aptamers for GBM diagnosis and 21 for GBM therapy. Our findings showed that a number of DNA and RNA aptamers are promising diagnostic and therapeutic tools for GBM management.

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Section: Resultsmentioning

confidence: 99%

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

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Brancato

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et al. 2020

Cancers

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“…It thus follows that detecting such morphological cues could greatly help in the identification of those cells that are most responsible for the metastatic process. Importantly, early identification of cancerous or pre-cancerous morphodynamic phenotypes could enable early detection of cancers which produce no known biomarkers, such as is the case for pancreatic cancer [ 28 , 41 ]. In future work, it would be beneficial to extend the dynamic morphodynamics platform to demonstrate sensitive differentiation of such cells, which could be of important diagnostic value.…”

Section: Discussionmentioning

confidence: 99%

“…Another study demonstrated that oncogenesis and metastasis were associated with characteristic changes in morphology [ 11 , 26 ]. While studies have been conducted that differentiate cancerous from non-cancerous cells [ 27 , 28 ], we have extended this analysis to compare metastatic with non-metastatic cell types. It would be of significant utility if a morphodynamic classification system could be built for suspended cells, such as circulating tumor cells (CTCs) or those harvested from a biopsy.…”

Section: Introductionmentioning

confidence: 99%

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

et al. 2021

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We define cell morphodynamics as the cell’s time dependent morphology. It could be called the cell’s shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.

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“…Current machine learning approaches of image detection such as logistic regression (LR), SVM (support vector machine), and LR + SVM 11,12 use hand‐made characteristics such as shape, pixel density, texture, and off‐shelf classification. Cell image features are classified on the bases of analysis of histopathological image features 13 and HEp‐2 cell IIF images, 14 detection of cancer cells by SVM and random forest tree, 15 and classification of apical echocardiograms 16 . Deep learning method is used to investigate medical injury 17 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning recognition of diseased and normal cell representation

Iqbal

Ahmad

Luo

et al. 2020

Trans Emerging Tel Tech

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Cell classification refers to detecting normal and diseased cells from small amount of data. Sometimes, classification of cells becomes difficult because some cells fall into more than one categories/classes. Current state‐of‐the‐art cell classification methods have been developed on the bases of tumor cell classification but these methods cannot classify diseased or normal cells. This study investigated the performance of two classification methods traditional machine learning and deep learning (normal and diseased cell classification) to categorize normal and diseased cells. Millions of normal cells undergo controlled growth and uncontrolled growth may be involved in disease causation but their clinical applications remain limited due to difficulties in distinguishing normal and diseased cells. Previous studies are limited to identify, systematically, the normal and diseased cells. This study collected information about diseased or normal cells to check the networks for correct cell detection and then eliminated false‐positive cells. We used machine learning methods along with logistic regression, support vector machine, and CNN (convolutional neural network). We found that our proposed method classified better the normal and diseased cells. With the help of two types of images: normal and diseased cells, we trained a CNN that identified diseased cells with 98% accuracy and enabled the discovery of normal and diseased cells. As a result, it will advance the clinical utility of human diseased cells.

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Classification of cancer cells using computational analysis of dynamic morphology

Cited by 29 publications

References 48 publications

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

Deep learning recognition of diseased and normal cell representation

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