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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Related Experiment Video

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Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and

Frederick M Howard, Hanna M Hieromnimon, Siddhi Ramesh

    Biorxiv : the Preprint Server for Biology
    |April 8, 2024
    PubMed
    Summary
    This summary is machine-generated.

    HistoXGAN reconstructs tumor histology from AI features, revealing biologic insights and enabling virtual biopsies. This artificial intelligence tool aids in understanding cancer subtypes and gene expression patterns.

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    Area of Science:

    • Computational pathology
    • Artificial intelligence in oncology
    • Digital pathology image analysis

    Background:

    • Artificial intelligence (AI) models analyze tumor histology for classification and molecular feature identification.
    • Current AI approaches distill histologic images into high-level features for prediction, but their biologic meaning is often unclear.
    • Understanding the biologic basis of AI-derived features in cancer histology is crucial for clinical translation.

    Approach:

    • Developed HistoXGAN, a custom generative adversarial network (GAN).
    • HistoXGAN reconstructs representative histology from feature vectors generated by common AI feature extractors.
    • Validated HistoXGAN across 29 diverse cancer subtypes.

    Key Points:

    • Reconstructed images from HistoXGAN retain biologically relevant information, including tumor grade, histologic subtype, and gene expression patterns.
    • HistoXGAN elucidates underlying histologic features driving AI predictions for actionable mutations.
    • The model identifies AI reliance on histologic batch effects and enables 'virtual biopsies' by reconstructing histology from radiographic imaging.

    Conclusions:

    • HistoXGAN provides a powerful tool for interpreting AI models in computational pathology.
    • This approach enhances understanding of the biologic underpinnings of AI-driven cancer analysis.
    • HistoXGAN facilitates the development of more robust and interpretable AI tools for precision oncology.