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A detailed dosimetric comparison between manual and inverse plans in HDR intracavitary/interstitial cervical cancer brachytherapy

  • Petra Trnková
  • , Dimos Baltas
  • , Andreas Karabis
  • , Markus Stock
  • , Johannes Dimopoulos
  • , Dietmar Georg
  • , Richard Pötter
  • , Christian Kirisits

Publikation: Beitrag in Fachzeitschrift (peer-reviewed)Artikel in Fachzeitschrift

Abstract

PURPOSE: The purpose of this study was to compare two inverse planning algorithms for cervical cancer brachytherapy and a conventional manual treatment planning according to the MUW (Medical University of Vienna) protocol.

MATERIAL AND METHODS: For 20 patients, manually optimized, and, inversely optimized treatment plans with Hybrid Inverse treatment Planning and Optimization (HIPO) and with Inverse Planning Simulated Annealing (IPSA) were created. Dosimetric parameters, absolute volumes of normal tissue receiving reference doses, absolute loading times of tandem, ring and interstitial needles, Paddick and COIN conformity indices were evaluated.

RESULTS: HIPO was able to achieve a similar dose distribution to manual planning with the restriction of high dose regions. It reduced the loading time of needles and the overall treatment time. The values of both conformity indices were the lowest. IPSA was able to achieve acceptable dosimetric results. However, it overloaded the needles. This resulted in high dose regions located in the normal tissue. The Paddick index for the volume of two times prescribed dose was outstandingly low.

CONCLUSIONS: HIPO can produce clinically acceptable treatment plans with the elimination of high dose regions in normal tissue. Compared to IPSA, it is an inverse optimization method which takes into account current clinical experience gained from manual treatment planning.

OriginalspracheEnglisch
Seiten (von - bis)163-170
Seitenumfang8
FachzeitschriftJournal of Contemporary Brachytherapy
Jahrgang2
Ausgabenummer4
DOIs
PublikationsstatusVeröffentlicht - Dez. 2010
Extern publiziertJa

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 3 – Gute Gesundheit und Wohlergehen
    SDG 3 – Gute Gesundheit und Wohlergehen

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