A tensor-based population value decomposition to explain rectal toxicity after prostate cancer radiotherapy

Juan David Ospina, Frédéric Commandeur, Richard Ríos, Gaël Dréan, Juan Carlos Correa, Antoine Simon, Pascal Haigron, Renaud De Crevoisier, Oscar Acosta

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

4 Citations (Scopus)

Abstract

In prostate cancer radiotherapy the association between the dose distribution and the occurrence of undesirable side-effects is yet to be revealed. In this work a method to perform population analysis by comparing the dose distributions is proposed. The method is a tensor-based approach that generalises an existing method for 2D images and allows for the highlighting of over irradiated zones correlated with rectal bleeding after prostate cancer radiotherapy. Thus, the aim is to contribute to the elucidation of the dose patterns correlated with rectal toxicity. The method was applied to a cohort of 63 patients and it was able to build up a dose pattern characterizing the difference between patients presenting rectal bleeding after prostate cancer radiotherapy and those who did not.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages387-394
Number of pages8
Volume16
EditionPART 2
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 22 Sept 201326 Sept 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8150 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/TerritoryJapan
CityNagoya
Period22/9/1326/9/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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