Entropy-based spatial heterogeneity analysis in pathological images for diagnostic applications

Rahul Suresh; Thi Nguyet Que Nguyen; Pascaline Bouzy; Nicholas Stone; Karin Jirstrom; Arman Rahman; William Gallagher; Aidan D. Meade

doi:10.1117/12.3022363

18 June 2024 Entropy-based spatial heterogeneity analysis in pathological images for diagnostic applications

Rahul Suresh, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William Gallagher, Aidan D. Meade

Author Affiliations +

Proceedings Volume 13011, Data Science for Photonics and Biophotonics; 130110E (2024) https://doi.org/10.1117/12.3022363
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Clinical pathological diagnosis and prognosis for cancer is often confounded by spatial tissue heterogeneity. This study investigates the utility of entropy as a robust quantitative metric of spatial disorder within Fourier Transform Infrared (FTIR) chemical images of breast cancer tissue. The use of entropy is grounded in its capacity to encapsulate the complexities of pixel-wise spectral intensity distributions, thus providing a detailed assessment of the spatial variations in biochemistry within tissue samples.

Here we explore the use of Shannon’s entropy as a single image-based metric of spectral biochemical heterogeneity within FTIR chemical images of breast cancer tissue. This metric was then analyzed statistically with respect to hormone receptor status. Our results suggest that while entropy effectively captures the heterogeneity of tissue samples, its role as a standalone predictor for diagnostic subtyping may be limited without considering additional variables or interaction effects. This work emphasizes the need for a multifaceted approach in leveraging entropy with chemical imaging for diagnostic subtyping in cancer.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rahul Suresh, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William Gallagher, and Aidan D. Meade "Entropy-based spatial heterogeneity analysis in pathological images for diagnostic applications", Proc. SPIE 13011, Data Science for Photonics and Biophotonics, 130110E (18 June 2024); https://doi.org/10.1117/12.3022363

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KEYWORDS

Tissues

FT-IR spectroscopy

Image information entropy

Diseases and disorders

Diagnostics

Cancer

Breast cancer

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