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Texture analysis to assess structural modifications induced by radiotherapy.

E Scalco, S Moriconi, G Rizzo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

Summary

This summary is machine-generated.

Texture analysis of parotid glands during head and neck cancer radiotherapy revealed decreased tissue complexity. Changes in gland structure correlated with radiation dose, offering insights into treatment effects.

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Area of Science:

Radiotherapy
Medical Imaging
Oncology

Background:

Radiotherapy (RT) for head and neck cancer can lead to high radiation doses in parotid glands.
High doses may compromise parotid gland functionality and structure, impacting salivary function.
Texture analysis is an emerging tool for evaluating tissue characteristics in medical imaging.

Purpose of the Study:

To apply texture analysis on CT images to evaluate structural changes in parotid glands during RT.
To assess the relationship between texture features, treatment time, and pre-treatment dosimetric parameters.

Main Methods:

Texture analysis was performed on CT images of head and neck cancer patients at three time points during RT (beginning, intermediate, end).
Features extracted included mean intensity, variance, entropy, homogeneity, local entropy, fractal dimension, and volume within the parotid gland.
Correlations between volume/intensity variations and pre-treatment dosimetric parameters were investigated.

Main Results:

A general decrease in parotid gland tissue complexity and heterogeneity was observed during RT.
Different textural features exhibited distinct time trends, suggesting varied underlying biological mechanisms.
Volume and mean intensity changes were correlated with pre-treatment dosimetric parameters.

Conclusions:

Texture analysis effectively quantifies structural changes in parotid glands during head and neck radiotherapy.
The observed changes indicate a relationship between radiation dose planning and post-treatment structural variations.
This technique holds potential for monitoring treatment effects and guiding future radiotherapy strategies.