TY - GEN
T1 - Regularization techniques in radiomics
T2 - 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019
AU - Carrasquinha, Eunice
AU - Santinha, João
AU - Mongolin, Alexander
AU - Lisitskiya, Maria
AU - Ribeiro, Joana
AU - Cardoso, Fátima
AU - Matos, Celso
AU - Vanneschi, Leonardo
AU - Papanikolaou, Nickolas
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT#
Carrasquinha, E., Santinha, J., Mongolin, A., Lisitskiya, M., Ribeiro, J., Cardoso, F., Matos, C., Vanneschi, L., & Papanikolaou, N. (2020). Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla. In P. Cazzaniga, D. Besozzi, I. Merelli, & L. Manzoni (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 16th International Meeting, CIBB 2019, Revised Selected Papers (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12313 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63061-4_24
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla.
AB - Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla.
KW - Breast cancer
KW - High-dimensional data
KW - Radiomic features
KW - Regularization techniques
UR - http://www.scopus.com/inward/record.url?scp=85098271604&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63061-4_24
DO - 10.1007/978-3-030-63061-4_24
M3 - Conference contribution
AN - SCOPUS:85098271604
SN - 9783030630607
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 271
EP - 281
BT - Computational Intelligence Methods for Bioinformatics and Biostatistics
A2 - Cazzaniga, Paolo
A2 - Besozzi, Daniela
A2 - Merelli, Ivan
A2 - Manzoni, Luca
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 September 2019 through 6 September 2019
ER -
