title: An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.
abstract: 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.
  
  
  
  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.
  
  
  
  The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced.
  
  
  
  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.
authors.affiliation: CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal.
authors.forename: Mariana A
authors.initials: MA
authors.lastname: Nogueira
authors.affiliation: CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal. pha@dei.uc.pt.
authors.forename: Pedro H
authors.initials: PH
authors.lastname: Abreu
authors.affiliation: CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal.
authors.forename: Pedro
authors.initials: P
authors.lastname: Martins
authors.affiliation: CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal.
authors.forename: Penousal
authors.initials: P
authors.lastname: Machado
authors.affiliation: IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.
authors.forename: Hugo
authors.initials: H
authors.lastname: Duarte
authors.affiliation: IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.
authors.forename: João
authors.initials: J
authors.lastname: Santos
mesh_terms: D000465:Algorithms; D005260:Female; D006689:Hodgkin Disease / Q000000981:diagnostic imaging* / Q000628:therapy; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D018358:Neuroendocrine Tumors / Q000000981:diagnostic imaging* / Q000628:therapy; D010363:Pattern Recognition, Automated / Q000379:methods; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods*; D015203:Reproducibility of Results; D016896:Treatment Outcome; D051598:Whole Body Imaging / Q000379:methods
keywords: Artificial neural networks; Images descriptors; PET/CT images; Treatment response assessment