Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer

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• 作者：Mariangela La Macchia (1)
  Francesco Fellin (1)
  Maurizio Amichetti (1)
  Marco Cianchetti (1)
  Stefano Gianolini (3)
  Vitali Paola (2)
  Antony J Lomax (3) (4)
  Lamberto Widesott (1) (4)
  
• 关键词：Automatic segmentation ; Adaptive radiotherapy ; Re ; planning
• 刊名：Radiation Oncology
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：7
• 期：1
• 全文大小：2521KB
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• 作者单位：Mariangela La Macchia (1)
  Francesco Fellin (1)
  Maurizio Amichetti (1)
  Marco Cianchetti (1)
  Stefano Gianolini (3)
  Vitali Paola (2)
  Antony J Lomax (3) (4)
  Lamberto Widesott (1) (4)
  
  1. Agenzia Provinciale per la Protonterapia, Via F.lli Perini, 181, 38122, Trento, Italy
  3. Center for Proton Radiation Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
  2. Istituto del Radio “O. Alberti- Spedali Civili, Brescia, Italy
  4. Department of Physics, Swiss Institute of Technology (ETH), Zurich, Switzerland
  

文摘

Purpose To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Methods and materials Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT) images, one replanning CT (rCT) image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs). We used three software solutions (VelocityAI 2.6.2 (V), MIM 5.1.1 (M) by MIMVista and ABAS 2.0 (A) by CMS-Elekta) to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC) were successively corrected manually. We recorded the time needed for: 1) ex novo ROIs definition on rCT; 2) generation of AC by the three software solutions; 3) manual correction of AC. To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE), sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z) from the isocenter. Results The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40?minutes for prostate, and about 20?minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate), A and M (contours for H&N), and M (contours for mesothelioma). Conclusions From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed.