Literature DB >> 35817048

Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging.

Nadya Shusharina1,2, Xiaofeng Liu2,3, Jaume Coll-Font2,4,5, Anna Foster4,5, Georges El Fakhri2,3, Jonghye Woo2,3, Thomas Bortfeld1,2, Christopher Nguyen2,4,5,6,7.   

Abstract

Objective.Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defining the boundary of the clinical target volume (CTV) in muscle tissue.Approach.We recruited eight healthy volunteers to acquire MR images of the left and right thigh. The imaging session consisted of (a) two MRI spin-echo-based scans, T1- and T2-weighted; (b) a diffusion weighted (DW) spin-echo-based scan using an echo planar acquisition with fat suppression. The thigh muscles were auto-segmented using the convolutional neural network. DT-MRI data were used as a geometry encoding input to solve the anisotropic Eikonal equation with the Hamiltonian Fast-Marching method. The isosurfaces of the solution modeled the CTV boundary.Main results.The auto-segmented muscles of the thigh agreed with manually delineated with the Dice score ranging from 0.8 to 0.94 for different muscles. To validate our method of deriving muscle fiber orientations, we compared anisotropy of the isosurfaces across muscles with different anatomical orientations within a thigh, between muscles in the left and right thighs of each subject, and between different subjects. The fiber orientations were identified reproducibly across all comparisons. We identified two controlling parameters, the distance from the gross tumor volume to the isosurface and the eigenvalues ratio, to tailor the proposed CTV to the satisfaction of the clinician.Significance.Our feasibility study with healthy volunteers shows the promise of using muscle fiber orientations derived from DW MRI data for automated generation of anisotropic CTV boundary in soft tissue sarcoma. Our contribution is significant as it serves as a proof of principle for combining DT-MRI information with tumor spread modeling, in contrast to using moderately informative 2D CT planes for the CTV delineation. Such improvements will positively impact the cancer centers with a small volume of sarcoma patients.
© 2022 Institute of Physics and Engineering in Medicine.

Entities:  

Keywords:  clinical target volume; diffusion weighted imaging; soft-tissue sarcoma

Mesh:

Year:  2022        PMID: 35817048      PMCID: PMC9344976          DOI: 10.1088/1361-6560/ac8045

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   4.174


  26 in total

1.  Quantitative diffusion tensor MRI-based fiber tracking of human skeletal muscle.

Authors:  Drew A Lansdown; Zhaohua Ding; Megan Wadington; Jennifer L Hornberger; Bruce M Damon
Journal:  J Appl Physiol (1985)       Date:  2007-04-19

2.  Wavelet-based Rician noise removal for magnetic resonance imaging.

Authors:  R D Nowak
Journal:  IEEE Trans Image Process       Date:  1999       Impact factor: 10.856

3.  RTOG sarcoma radiation oncologists reach consensus on gross tumor volume and clinical target volume on computed tomographic images for preoperative radiotherapy of primary soft tissue sarcoma of extremity in Radiation Therapy Oncology Group studies.

Authors:  Dian Wang; Walter Bosch; David Roberge; Steven E Finkelstein; Ivy Petersen; Michael Haddock; Yen-Lin E Chen; Naoyuki G Saito; David G Kirsch; Ying J Hitchcock; Aaron H Wolfson; Thomas F DeLaney
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-06-15       Impact factor: 7.038

4.  An exploration of diffusion tensor eigenvector variability within human calf muscles.

Authors:  Conrad Rockel; Michael D Noseworthy
Journal:  J Magn Reson Imaging       Date:  2015-05-27       Impact factor: 4.813

5.  Interobserver variability of clinical target volume delineation in soft-tissue sarcomas.

Authors:  D Genovesi; G Ausili Cèfaro; M Trignani; A Vinciguerra; A Augurio; M Di Tommaso; F Perrotti; A De Paoli; P Olmi; V Valentini; M Di Nicola
Journal:  Cancer Radiother       Date:  2014-01-17       Impact factor: 1.018

6.  Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation.

Authors:  Xiaofeng Liu; Fangxu Xing; Chao Yang; Georges El Fakhri; Jonghye Woo
Journal:  Med Image Comput Comput Assist Interv       Date:  2021-09-21

Review 7.  Cancer invasion into musculature: Mechanics, molecules and implications.

Authors:  Lianne Beunk; Kari Brown; Iris Nagtegaal; Peter Friedl; Katarina Wolf
Journal:  Semin Cell Dev Biol       Date:  2018-09-05       Impact factor: 7.727

8.  Denoising diffusion-weighted magnitude MR images using rank and edge constraints.

Authors:  Fan Lam; S Derin Babacan; Justin P Haldar; Michael W Weiner; Norbert Schuff; Zhi-Pei Liang
Journal:  Magn Reson Med       Date:  2014-03       Impact factor: 4.668

9.  Dipy, a library for the analysis of diffusion MRI data.

Authors:  Eleftherios Garyfallidis; Matthew Brett; Bagrat Amirbekian; Ariel Rokem; Stefan van der Walt; Maxime Descoteaux; Ian Nimmo-Smith
Journal:  Front Neuroinform       Date:  2014-02-21       Impact factor: 4.081

10.  Accelerated in Vivo Cardiac Diffusion-Tensor MRI Using Residual Deep Learning-based Denoising in Participants with Obesity.

Authors:  Kellie Phipps; Maaike van de Boomen; Robert Eder; Sam Allen Michelhaugh; Aferdita Spahillari; Joan Kim; Shestruma Parajuli; Timothy G Reese; Choukri Mekkaoui; Saumya Das; Denise Gee; Ravi Shah; David E Sosnovik; Christopher Nguyen
Journal:  Radiol Cardiothorac Imaging       Date:  2021-06-24
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