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Cancer stem cells deep learning 

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Deep learning research is being increasingly applied to the field of cancer stem cells (CSCs). CSCs are a subset of tumor cells that drive tumor initiation, relapse, and metastasis. Recent advances in artificial intelligence (AI) technology, specifically convolutional neural networks (CNNs), have enabled automated recognition of CSCs from biological images. AI models, such as conditional generative adversarial networks (CGANs), have been used to predict and identify CSCs with high accuracy from phase-contrast images . Additionally, deep learning-based algorithms have been developed to monitor the fate of isolated CSCs in real-time, providing label-free detection and analysis . These deep learning models have shown promising results in accurately predicting the microsatellite status and POLE mutation of CRC primary tumors, which can be used to stratify patients for immunotherapy . Overall, deep learning techniques have emerged as valuable tools for studying and understanding CSCs in cancer research.

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The paper discusses the emerging trend of using deep learning models, specifically convolutional neural networks (CNNs), for cancer stem cell (CSC) research. It explores the application of deep learning in automated recognition of CSCs from biological images.
The paper discusses the use of artificial intelligence (AI) technology, specifically deep learning, for the identification of cancer stem cells (CSCs) in cultures and tissues. The authors propose a workflow using a conditional generative adversarial network (CGAN) image translation model to improve the accuracy of CSC prediction from phase-contrast images.
The paper discusses the use of deep learning for the recognition and segmentation of cancer stem cells (CSCs) in microscopy images. It emphasizes the importance of training datasets from multiple days of cultivation to improve the accuracy of AI models.
The paper discusses the use of deep learning algorithms to detect and track the fate of cancer stem-like cells in non-adherent conditions.

Related Questions

What are cancer stem cells?5 answersCancer stem cells (CSCs) are a subpopulation of tumor cells that possess the characteristics of self-renewal and clonogenicity. They have been identified in various malignancies, including leukemia and solid tumors. CSCs play a crucial role in tumor initiation, propagation, metastasis, and resistance to therapy. They contribute to the heterogeneity of tumors and control the tumor microenvironment, promoting epithelial-to-mesenchymal transition (EMT) and altering cellular signaling. CSCs are characterized by their stemness properties, including multi-directional differentiation capacity, quiescence/dormancy, and tumor-forming capability. The crosstalk between CSCs and the tumor microenvironment modulates tumor heterogeneity. Understanding the molecular signatures of CSCs and their derived exosomes has potential implications for cancer theranostics development and clinical screening biomarkers for cancer. Therapeutic targeting of CSCs is an important area of research for improving cancer treatment outcomes.
Cancer treatment with stem cells4 answersStem cell therapy shows promise in the treatment of cancer, including metastatic cancer. Stem cells can be used as a platform to deliver drugs to tumor targets, decreasing tumor volume and enhancing survival rates in pre-clinical trials. Stem cell transplantation, also known as bone marrow transplantation, is a procedure used to treat certain types of cancer. It involves replacing damaged or diseased bone marrow with healthy stem cells, which can grow and develop into new blood cells. Cancer stem cells (CSCs) have been isolated in glioblastoma, a malignant brain tumor, and studies have shown their resistance to chemotherapy and radiotherapy. Oncolytic herpes simplex virus (oHSV) vectors have been tested as a therapeutic strategy for GBM-SCs, showing efficacy in killing the cells and inhibiting their self-renewal. Stem cells, which are inherently tumoritropic, can be used as a drug delivery vehicle to target both primary and metastatic tumors. Intravenous infusions of stem cells carrying or secreting therapeutic payloads have shown promise in the treatment of cancer. Stem cells, including colorectal cancer (CRC) stem cells, mesenchymal stromal cells (MSCs), and resident stromal cells (RSCs) of the tumor microenvironment, have potential in personalized CRC treatment and improving current treatment approaches.
Cancer and stem cells5 answersCancer stem cells (CSCs) are a subpopulation of cells within tumors that display stem cell properties. These cells play a crucial role in tumor initiation, propagation, metastasis, and resistance to therapy. CSCs have the ability to differentiate into various cell types, including tumor stromal cells, such as endothelial cells, fibroblasts, and immune cells. They also share similarities with embryonic stem cells (ESCs) and hijack pluripotent transcription factors and developmental signaling pathways, known as oncofetal drivers, which are ideal therapeutic targets. CSCs contribute to tumor-associated angiogenesis through trans-differentiation, vasculogenic mimicry, and facilitation of endothelial cell-involved angiogenesis. Targeting CSCs and their associated pathways is crucial for effective cancer treatment and prevention of recurrence. Further research is needed to identify novel targets and develop therapeutic approaches to interfere with CSC-mediated neovascularization and improve cancer treatment outcomes.
Cancer prediction using deep learning5 answersCancer prediction using deep learning has been investigated in several studies. One study focused on using deep learning to analyze conventional portrait photographs and extract prognostic information for survival prediction in cancer patients. Another study developed an attention-based multimodal deep learning model that combined clinical, copy number alteration, and gene expression data to improve the accuracy of breast cancer prediction. Additionally, a minireview summarized the advances in integrating multi-omics data and using deep learning models for cancer prognosis prediction, highlighting the potential of deep learning in revolutionizing cancer prognosis. Furthermore, a hybrid technique called CSSMO was proposed, which combined feature selection using metaheuristic algorithms with deep learning for cancer prediction, achieving better prediction accuracy compared to other models. These studies demonstrate the potential of deep learning in cancer prediction and prognosis.
Cancer prognosis prediction deep learning4 answersDeep learning (DL) has emerged as a technology of choice for cancer prognosis prediction. DL models require minimal data manipulations and achieve better results while working with enormous volumes of data. They have been applied to determine the best models for cancer diagnosis and prognosis prediction tasks. Machine learning (ML) models, including deep learning, have been employed for modeling the development and treatment of cancer conditions. These models have shown high detection rates and precise decision-making capabilities. Artificial intelligence (AI) and deep learning techniques have been used to develop computationally efficient models for the detection of cancerous conditions. They have shown promise in predicting outcomes for various types of cancer. A method of deep learning combined with computed tomography (CT) imaging has been designed for survival prediction in non-small cell lung cancer (NSCLC) patients. This method has yielded high accuracy and has important application value in establishing individualized prognostic models. AI, ML, and DL algorithms have been incorporated into the healthcare system to predict cancer risk, diagnose cancer, and predict cancer prognosis. These advancements have improved patient outcomes and personalized treatment planning.
Cancer Stem cells in cell lines5 answersCancer stem cells (CSCs) have been identified and characterized in various cancer cell lines. These CSCs are responsible for tumor progression, metastasis, and relapse. Isolation and characterization of CSCs have been achieved using different methods, such as single cell isolation and flow cytometry. CSCs have been found to exhibit stem cell properties, including self-renewal, high proliferation, and resistance to chemotherapy and radiotherapy. They also express specific markers, such as CD44, CD24, and CD133, which are commonly used in CSC research. The presence of CSCs in cancer cell lines has been confirmed through experiments, showing their enriched tumor-initiating capability and invasiveness. These findings highlight the importance of CSCs as therapeutic targets and tools for investigating the tumorigenic process.

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