Detection of non‐small cell lung cancer cells based on microfluidic polarization microscopic image analysis

Wang, Yanjuan; Wang, Junsheng; Meng, Jie; Ding, Gege; Shi, Zhengwei; Wang, Ruoyu; Zhang, Xiaohui

doi:10.1002/elps.201800284

Cited by 13 publications

(4 citation statements)

References 43 publications

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“…Rossi et al showed that CD4 and CD8 T lymphocytes can be differentiated by their scattered light due to biophysical property differences [203]. Polarization microscopy can also be conjugated with machine learning for the detection and classification of non-small-cell lung cancer cells without any staining [204].…”

Section: Cell Counting and Classificationmentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

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Section: Cell Counting and Classificationmentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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“…Quadratic discriminant analysis is used in the classification and differentiation of dissimilar types of label-free tumor cells [ 35 ]. LR-based linear classifiers use microfluidic imaging and analysis to capture and isolate cancer-related fibroblasts and two different cell types of lung cancer [ 36 ]. A decision tree can help classify label-free cancer cells in blood with continuous flow in microfluidic channels.…”

Section: Systematic Descriptionmentioning

confidence: 99%

Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Liu

2022

Cells

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Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.

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“…However, recent research in related areas has applied machine learning more generally to microfluidic platforms. For example, Wang et al (2018) developed a singlechannel microfluidic device and used polarization microscopy to classify CAFs and two different non-small cell lung cancer cell lines, A549 and H322, via logistic regression and gradient descent with regularization. Their classification algorithm achieved 66.7% accuracy.…”

Section: Chen Et Al 2017mentioning

confidence: 99%

Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis

Sigdel

Gupta

Faizee

et al. 2021

Front. Bioeng. Biotechnol.

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Of around half a million women dying of breast cancer each year, more than 90% die due to metastasis. Models necessary to understand the metastatic process, particularly breast cancer cell extravasation and colonization, are currently limited and urgently needed to develop therapeutic interventions necessary to prevent breast cancer metastasis. Microfluidic approaches aim to reconstitute functional units of organs that cannot be modeled easily in traditional cell culture or animal studies by reproducing vascular networks and parenchyma on a chip in a three-dimensional, physiologically relevant in vitro system. In recent years, microfluidics models utilizing innovative biomaterials and micro-engineering technologies have shown great potential in our effort of mechanistic understanding of the breast cancer metastasis cascade by providing 3D constructs that can mimic in vivo cellular microenvironment and the ability to visualize and monitor cellular interactions in real-time. In this review, we will provide readers with a detailed discussion on the application of the most up-to-date, state-of-the-art microfluidics-based breast cancer models, with a special focus on their application in the engineering approaches to recapitulate the metastasis process, including invasion, intravasation, extravasation, breast cancer metastasis organotropism, and metastasis niche formation.

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Detection of non‐small cell lung cancer cells based on microfluidic polarization microscopic image analysis

Cited by 13 publications

References 43 publications

Microsystem Advances through Integration with Artificial Intelligence

Microsystem Advances through Integration with Artificial Intelligence

Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis

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