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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach.

Chang Bian1, Garry Ashton2, Megan Grant2

  • 1The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK.

Cancers
|June 19, 2024
PubMed
Summary

Cellular morphology, specifically nuclei size in the invasive band, shows prognostic value in melanoma. This computational analysis complements traditional indicators, suggesting new avenues for melanoma prognostication.

Keywords:
cellular morphologycomputational pipelinedeep learningmachine learningmelanoma prognosticationspatial analysis

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Area of Science:

  • Oncology
  • Computational Pathology
  • Dermatology

Background:

  • Traditional melanoma prognostication relies on factors like mitotic activity and tumor thickness.
  • Cellular morphology and spatial configurations offer potential complementary prognostic information.
  • Integrating advanced computational methods can enhance the analysis of these features.

Purpose of the Study:

  • To investigate the prognostic value of cellular morphology and spatial configurations in melanoma.
  • To quantify nuclei sizes in different spatial regions using machine learning and deep learning.
  • To correlate these morphological features with established prognostic indicators and patient outcomes.

Main Methods:

  • Development of a computational pipeline utilizing machine learning and deep learning algorithms.
  • Quantification of nuclei sizes within distinct spatial regions of melanoma tissue.
  • Application of univariate and multivariate Cox models to assess prognostic significance.
  • Correlation analysis to explore relationships between cellular morphology and tumor characteristics.

Main Results:

  • Nuclei sizes in the invasive band were found to be significant prognostic factors (HR=1.1).
  • Nuclei sizes of tumor cells and Ki67/S100 co-positive cells in the invasive band also showed prognostic significance (HRs of 1.07 and 1.09, respectively).
  • A substantial correlation was observed between nuclei size in the invasive band and tumor thickness.

Conclusions:

  • Nuclei size, particularly within the invasive band, serves as a potential prognostic factor in melanoma.
  • Spatial and morphological analyses, including nuclei size, can complement existing prognostic tools.
  • These findings support the integration of computational pathology into melanoma prognostication strategies.