A statistical approach for achievable dose querying in IMRT planning

Patricio Simari, Binbin Wu, Robert Jacques, Alex King, Todd McNutt, Russell Taylor, Michael Kazhdan

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

Abstract

The task of IMRT planning, particularly in head-and-neck cancer, is a difficult one, often requiring days of work from a trained dosimetrist. One of the main challenges is the prescription of achievable target doses that will be used to optimize a treatment plan. This work explores a data-driven approach in which effort spent on past plans is used to assist in the planning of new patients. Using a database of treated patients, we identify the features of patient geometry that are correlated with received dose and use these to prescribe target dose levels for new patients. We incorporate our approach in a quality-control system, identifying patients with organs that received a dose significantly higher than the one recommended by our method. For all these patients, we have found that a replan using our predicted dose results in noticeable sparing of the organ without compromising dose to other treatment volumes.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Pages521-528
Number of pages8
EditionPART 3
DOIs
StatePublished - Nov 22 2010
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China
Duration: Sep 20 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6363 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
CountryChina
CityBeijing
Period9/20/109/24/10

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Keywords

  • Data-driven IMRT planning
  • Overlap Volume Histogram
  • achievable dose querying

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Simari, P., Wu, B., Jacques, R., King, A., McNutt, T., Taylor, R., & Kazhdan, M. (2010). A statistical approach for achievable dose querying in IMRT planning. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 3 ed., pp. 521-528). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-15711-0_65