Abstract

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation.

Highlights

  • Radiation therapy is a standard treatment for approximately 50% of all cancer patients [1]

  • We have demonstrated that Raman spectroscopy can offer multiplexed, biologically significant molecular-level information on cellular and tumour radiation response [8]

  • While we tackle the radiobiological implications of glycogen production in a separate work [30], it is clear that the Haralick features calculated here are (i) able to quantitate the extent of textural variation as a function of radiation dose, and (ii) correlate well with expected radiobiological trends in our murine models

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Summary

Introduction

Radiation therapy is a standard treatment for approximately 50% of all cancer patients [1]. While significant improvements in the technological development of radiation therapy have occurred in the past several decades, a number of challenges in treatment efficacy remain unmet. Among these challenges, optimizing, or personalizing, radiation therapy remains. Quantification of heterogeneity in tumour radiation response

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