Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach

In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we...

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Bibliographic Details
Published in:Cancers 2024-05, Vol.16 (11), p.2026
Main Authors: Bian, Chang, Ashton, Garry, Grant, Megan, Rodriguez, Valeria Pavet, Martin, Isabel Peset, Tsakiroglou, Anna Maria, Cook, Martin, Fergie, Martin
Format: Article
Language:English
Subjects:
Antibodies
Cancer
Cell physiology
Computer applications
Correlation analysis
Cytology
Deep learning
Machine learning
Medical prognosis
Melanoma
Morphology
Nuclei
Oncology, Experimental
Physical characteristics
Prognosis
Skin cancer
Spatial analysis
Spatial distribution
Statistical models
Survival analysis
Tumor cells
Tumors
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Summary:In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we quantified nuclei sizes within different spatial regions and analyzed their prognostic significance using univariate and multivariate Cox models. Nuclei sizes in the invasive band demonstrated a significant hazard ratio (HR) of 1.1 (95% CI: 1.03, 1.18). Similarly, the nuclei sizes of tumor cells and Ki67 S100 co-positive cells in the invasive band achieved HRs of 1.07 (95% CI: 1.02, 1.13) and 1.09 (95% CI: 1.04, 1.16), respectively. Our findings reveal that nuclei sizes, particularly in the invasive band, are potentially prognostic factors. Correlation analyses further demonstrated a meaningful relationship between cellular morphology and tumor progression, notably showing that nuclei size within the invasive band correlates substantially with tumor thickness. These results suggest the potential of integrating spatial and morphological analyses into melanoma prognostication.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16112026