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Title: Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

Abstract

Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significantmore » differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue segmentation methods should be used with caution in patients with sparse fibroglandular tissue distribution.« less

Authors:
 [1];  [1];  [2];  [1]
  1. Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom)
  2. Department of Academic Radiotherapy, Royal Marsden National Health Service Foundation Trust, Sutton (United Kingdom)
Publication Date:
OSTI Identifier:
22149614
Resource Type:
Journal Article
Journal Name:
International Journal of Radiation Oncology, Biology and Physics
Additional Journal Information:
Journal Volume: 84; Journal Issue: 3; Other Information: Copyright (c) 2012 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0360-3016
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; ALGORITHMS; CALORIMETRY; CAT SCANNING; COMPARATIVE EVALUATIONS; DATASETS; FUZZY LOGIC; MAMMARY GLANDS; NEOPLASMS; PATIENTS; PLANNING; RADIOTHERAPY; SIMULATION; TISSUE DISTRIBUTION

Citation Formats

Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk, Harris, Emma J., Kirby, Anna M., and Evans, Philip M. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study. United States: N. p., 2012. Web. doi:10.1016/J.IJROBP.2012.05.014.
Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk, Harris, Emma J., Kirby, Anna M., & Evans, Philip M. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study. United States. doi:10.1016/J.IJROBP.2012.05.014.
Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk, Harris, Emma J., Kirby, Anna M., and Evans, Philip M. Thu . "Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study". United States. doi:10.1016/J.IJROBP.2012.05.014.
@article{osti_22149614,
title = {Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study},
author = {Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk and Harris, Emma J. and Kirby, Anna M. and Evans, Philip M.},
abstractNote = {Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue segmentation methods should be used with caution in patients with sparse fibroglandular tissue distribution.},
doi = {10.1016/J.IJROBP.2012.05.014},
journal = {International Journal of Radiation Oncology, Biology and Physics},
issn = {0360-3016},
number = 3,
volume = 84,
place = {United States},
year = {2012},
month = {11}
}