Literature DB >> 29526492

Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors.

Vikas Gupta1, Yibing Wang2, Alejandra Méndez Romero2, Andriy Myronenko3, Petr Jordan3, Calvin Maurer3, Ben Heijmen2, Mischa Hoogeman2.   

Abstract

PURPOSE: To validate a novel deformable image registration (DIR) method for online adaptation of planning organ-at-risk (OAR) delineations to match daily anatomy during hypo-fractionated RT of abdominal tumors.
MATERIALS AND METHODS: For 20 liver cancer patients, planning OAR delineations were adapted to daily anatomy using the DIR on corresponding repeat CTs. The DIR's accuracy was evaluated for the entire cohort by comparing adapted and expert-drawn OAR delineations using geometric (Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD) and Mean Surface Error (MSE)) and dosimetric (Dmax and Dmean) measures.
RESULTS: For all OARs, DIR achieved average DSC, MHD and MSE of 86%, 2.1 mm, and 1.7 mm, respectively, within 20 s for each repeat CT. Compared to the baseline (translations), the average improvements ranged from 2% (in heart) to 24% (in spinal cord) in DSC, and 25% (in heart) to 44% (in right kidney) in MHD and MSE. Furthermore, differences in dose statistics (Dmax, Dmean and D2%) using delineations from an expert and the proposed DIR were found to be statistically insignificant (p > 0.01).
CONCLUSION: The validated DIR showed potential for online-adaptive radiotherapy of abdominal tumors as it achieved considerably high geometric and dosimetric correspondences with the expert-drawn OAR delineations, albeit in a fraction of time required by experts.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cyberknife; Dose sparing; Image registration; Liver; Online adaptive radiotherapy; SBRT

Mesh:

Year:  2018        PMID: 29526492     DOI: 10.1016/j.radonc.2018.02.014

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  2 in total

1.  General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

Authors:  Asma Amjad; Jiaofeng Xu; Dan Thill; Colleen Lawton; William Hall; Musaddiq J Awan; Monica Shukla; Beth A Erickson; X Allen Li
Journal:  Med Phys       Date:  2022-02-07       Impact factor: 4.071

2.  Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT.

Authors:  Alba Magallon-Baro; Maaike T W Milder; Patrick V Granton; Wilhelm den Toom; Joost J Nuyttens; Mischa S Hoogeman
Journal:  Front Oncol       Date:  2022-06-08       Impact factor: 5.738

  2 in total

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