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Updated: Jun 27, 2026

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
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Towards Accurate Breslow Measurements: Mitigating Issues in Histopathological Imaging.

Nico Curti1, Lorenzo Dall'Olio2, Giulia Veronesi3,4

  • 1Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

Manual Breslow thickness measurement in melanoma is challenging due to operator variability. A new semi-automated Computer Vision software offers a more robust estimation, showing AI can reduce expert overestimation in melanoma staging.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Science

Background:

  • Breslow thickness is crucial for cutaneous melanoma staging.
  • Manual measurement faces challenges due to operator dependency and epidermal morphology.
  • Accurate measurement is vital for prognosis and treatment decisions.

Purpose of the Study:

  • To develop and evaluate a semi-automated Computer Vision (CV) software for robust Breslow thickness estimation.
  • To quantify inter-operator variability in manual Breslow thickness measurements.
  • To compare AI-driven measurements with expert histopathologist assessments.

Main Methods:

  • Development of a semi-automated CV software for Breslow thickness measurement.
  • Seven histopathologists measured Breslow thickness on 40 Whole Slide Images (WSIs) of pT1a melanomas.
Keywords:
Breslowartificial intelligencecomputer visiondigital pathologymelanoma

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  • Comparison of manual measurements against AI results, assessing inter-operator agreement and angular variance.

    • Main Results:

    • Significant variability in manual measurement orientation was observed among operators.
    • Epidermal irregularity correlated linearly with increased measurement uncertainty (angular variance).
    • The AI system showed statistically significant differences with five of seven operators, indicating a tendency for experts to overestimate Breslow thickness.

    Conclusions:

    • Semi-automated CV software provides a more robust Breslow thickness estimation compared to manual methods.
    • AI-based measurement can mitigate operator dependency and potential overestimation in melanoma staging.
    • This technology has the potential to improve the accuracy and consistency of melanoma prognostic parameter assessment.