An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.

BACKGROUND

Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored.

METHODS

In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT.

RESULTS

The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced.

CONCLUSIONS

After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.