Early-stage cancer treatment, driven by context of molecular imaging (ESTIMA)
Description
The primary goal of this prospective observational project proposed by INSTITUTO PORTUGUES DE ONCOLOGIA DO PORTO FRANCISCO GENTIL, EPE is to determine the personalized patient pharmacogenetic profile to evaluate the effectiveness of the current treatment (neoadjuvant radio/chemotherapy followed by surgery) approach for oesophagic early-stage cancers. The project aims to determine a clear definition of the criteria for non- surgical (conservative) approach of these patients.Researchers
João A M Santos (coordinator)
Miriam Seoane Santos
Inês Campos Monteiro Sabino Domingues
José Pedro Pereira Amorim
Miriam Seoane Santos
Inês Campos Monteiro Sabino Domingues
José Pedro Pereira Amorim
Funded by
Project NORTE-01-0145-FEDER-000027, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)Partners
INSTITUTO PORTUGUES DE ONCOLOGIA DO PORTO FRANCISCO GENTIL EPE (Instituição proponente)Keywords
Oesophagic cancer; Precision Medicine; Pattern Recognition; Machine learning; Image processing;Start Date
2016-03-01End Date
2019-03-31Conference Articles
2018
(4 publications)- Inês Domingues and Martins, P. and Pedro Henriques Abreu , "Registration of CT with PET: a comparison of intensity-based approaches", in International Workshop on Combinatorial Image Analysis (IWCIA), 2018
- Inês Domingues and Pedro Henriques Abreu , "BI-RADS classification of breast cancer: a new pre-processing pipeline for deep models training", in IEEE International Conference on Image Processing (ICIP), 2018
- Inês Domingues and Amorim, J.P.P. and Pedro Henriques Abreu , "Evaluation of oversampling data balancing techniques in the context of ordinal classification", in International Joint Conference on Neural Networks (IJCNN), 2018
- Amorim, J.P.P. and Inês Domingues and Pedro Henriques Abreu , "Interpreting Deep Learning Models for Ordinal Problems", in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018