Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Quantitative effects of intercellular signals on computer-simulated tumor patterns

J Smolle1, R Hofmann-Wellenhof

  • 1Department of Dermatology, University of Graz, Austria.

Analytical and Quantitative Cytology and Histology
|June 1, 1993
PubMed
Summary

Intercellular signals significantly impact tumor growth patterns. Computer simulations reveal that autocrine and paracrine factors quantitatively alter tumor cell proliferation, motility, and death, influencing multiple morphologic features.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The art of diagnosing rare skin tumors: Can DL-CNNs enhance dermatologists' diagnostic accuracy?

European journal of cancer (Oxford, England : 1990)·2025
Same author

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

European journal of cancer (Oxford, England : 1990)·2024
Same author

Hyperbaric Oxygen Therapy and Tissue Regeneration: A Literature Survey.

Biomedicines·2022
Same author

Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration.

Journal of the European Academy of Dermatology and Venereology : JEADV·2020
Same author

[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete·2020
Same author

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

Annals of oncology : official journal of the European Society for Medical Oncology·2020

Area of Science:

  • Computational biology
  • Cancer research
  • Mathematical modeling

Background:

  • Tumor cell behavior is influenced by the in vivo microenvironment.
  • Intercellular signals, including autocrine and paracrine factors, play a role in tumor growth.
  • Previous simulations showed qualitative effects of these signals on tumor morphology.

Purpose of the Study:

  • To quantitatively assess the effect of intercellular signals on tumor growth patterns.
  • To evaluate the impact of autocrine and paracrine signaling on specific morphologic features.
  • To establish relationships between signaling factors and quantitative pattern measurements.

Main Methods:

  • Computer simulations of tumor growth were employed.
  • Pattern analysis procedures were used to derive 18 quantitative morphologic features.

Related Experiment Videos

  • Linear regression and multivariate analysis were applied to assess signal-feature relationships.

    • Main Results:

    • Both autocrine and paracrine factors significantly influenced at least 10 of 18 morphologic features (P < .01).
    • Multivariate analysis of 600 simulated patterns showed highly significant relationships between intercellular signals and pattern features (P < .001).
    • Specific signaling pathways (proliferation, motility, death) were linked to pattern changes.

    Conclusions:

    • Intercellular signals exert a direct and significant quantitative influence on tumor patterns in simulations.
    • Quantitative morphologic features can serve as indicators of intercellular signaling effects.
    • This study provides a framework for understanding signal-driven tumor evolution in silico.