Radiomics to predict response to neoadjuvant chemotherapy in rectal cancer: Influence of simultaneous feature selection and classifier optimization

S. Rosati, C. M. Gianfreda, G. Balestra, V. Giannini, S. Mazzetti, D. Regge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

According to the guidelines, patients with locally advanced colorectal cancer undergo neoadjuvant chemotherapy. However, response to therapy is reached only up to 30% of cases. Therefore, it would be important to predict response to therapy before treatment. In this study, we demonstrated that the simultaneous optimization of feature subset and classifier parameters on different imaging datasets (T2w, DWI and PET) could improve classification performance. On a dataset of 51 patients (21 responders, 30 non responders), we obtained an accuracy of 90%, 84% and 76% using three optimized SVM classifiers fed with selected features from PET, T2w and ADC images, respectively.

Original languageEnglish
Title of host publication2018 IEEE Life Sciences Conference, LSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-68
Number of pages4
ISBN (Electronic)9781538667095
DOIs
Publication statusPublished - Dec 10 2018
Event2018 IEEE Life Sciences Conference, LSC 2018 - Montreal, Canada
Duration: Oct 28 2018Oct 30 2018

Conference

Conference2018 IEEE Life Sciences Conference, LSC 2018
Country/TerritoryCanada
CityMontreal
Period10/28/1810/30/18

Keywords

  • Feature selection
  • Genetic algorithms
  • Rectal cancer
  • Response to chemoradiotherapy
  • SVM optimization

ASJC Scopus subject areas

  • Signal Processing
  • Medicine (miscellaneous)
  • Health Informatics
  • Instrumentation

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