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

Suresh, Rahul ; Nguyen, Thi Nguyet Que ; Stone, Nicholas ; Jirström, Karin LU orcid ; Rahman, Arman ; Gallagher, William and Meade, Aidan D (2024) Data Science for Photonics and Biophotonics 2024 In Proceedings of SPIE - The International Society for Optical Engineering 13011.
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Breast cancer, Fourier Transform Infrared (FTIR) chemical imaging, Shannon’s entropy (entropy)
host publication
Data Science for Photonics and Biophotonics
series title
Proceedings of SPIE - The International Society for Optical Engineering
editor
Bocklitz, Thomas
volume
13011
publisher
SPIE
conference name
Data Science for Photonics and Biophotonics 2024
conference location
Strasbourg, France
conference dates
2024-04-07 - 2024-04-11
external identifiers
  • scopus:85200263296
ISSN
1996-756X
0277-786X
ISBN
9781510673403
DOI
10.1117/12.3022363
language
English
LU publication?
yes
id
cb088237-950b-40b1-8131-513b747fc9af
date added to LUP
2024-10-27 18:10:04
date last changed
2025-07-08 02:09:29
@inproceedings{cb088237-950b-40b1-8131-513b747fc9af,
  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.}},
  author       = {{Suresh, Rahul and Nguyen, Thi Nguyet Que and Stone, Nicholas and Jirström, Karin and Rahman, Arman and Gallagher, William and Meade, Aidan D}},
  booktitle    = {{Data Science for Photonics and Biophotonics}},
  editor       = {{Bocklitz, Thomas}},
  isbn         = {{9781510673403}},
  issn         = {{1996-756X}},
  keywords     = {{Breast cancer; Fourier Transform Infrared (FTIR) chemical imaging; Shannon’s entropy (entropy)}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Proceedings of SPIE - The International Society for Optical Engineering}},
  title        = {{Entropy-based spatial heterogeneity analysis in pathological images for diagnostic applications}},
  url          = {{http://dx.doi.org/10.1117/12.3022363}},
  doi          = {{10.1117/12.3022363}},
  volume       = {{13011}},
  year         = {{2024}},
}