Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla

Eunice Carrasquinha, João Santinha, Alexander Mongolin, Maria Lisitskiya, Joana Ribeiro, Fátima Cardoso, Celso Matos, Leonardo Vanneschi, Nickolas Papanikolaou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics
Subtitle of host publication16th International Meeting, CIBB 2019, Revised Selected Papers
EditorsPaolo Cazzaniga, Daniela Besozzi, Ivan Merelli, Luca Manzoni
PublisherSpringer Science and Business Media Deutschland GmbH
Pages271-281
Number of pages11
ISBN (Print)9783030630607
DOIs
Publication statusPublished - 10 Dec 2020
Event16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019 - Bergamo, Italy
Duration: 4 Sep 20196 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12313 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019
CountryItaly
CityBergamo
Period4/09/196/09/19

Keywords

  • Breast cancer
  • High-dimensional data
  • Radiomic features
  • Regularization techniques

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